-------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\Felix Wolter\Documents\Corona\Analyse\Article DiehlWolter2021\Diehl Wolter Log File.log
  log type:  text
 opened on:  25 Jun 2021, 09:05:33

. version 16.1

. set more off, permanently
(set more preference recorded)

. set linesize 255

. clear all

. cls

. 
. cd "C:\Users\Felix Wolter\Documents\Corona\Analyse\Article DiehlWolter2021"
C:\Users\Felix Wolter\Documents\Corona\Analyse\Article DiehlWolter2021

. 
. *Prepare Spring 2020 survey
. do varprepspring.do

. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *
. * Diehl/Wolter: ATTITUDES ABOUT CONTAINMENT MEASURES DURING THE
. *               2020/2021 CORONAVIRUS PANDEMIC:
. *               SELF-INTEREST, OR BROADER POLITICAL ORIENTATIONS?
. *
. * Do-File for variable preparation of the spring 2020 wave
. *
. * This version: 20210430
. *
. * Author: Felix Wolter, felix.wolter@uni-konstanz.de
. *
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. *Load Spring 2020 Kantar survey
. use "kantarcovidratesmay.dta", clear
(Corona-survey Cluster of Excellence 'The Politics of Inequality' - Kantar)

. numlabel _all, add

. 
. *Generate empty sample variable existing only in wave 2
. gen sample=.
(3,278 missing values generated)

. 
. *Wave (time) identifier
. gen time = 0

. label variable time "Time/wave"

. label define time 0 "spring 2020" 1 "november 2020"

. label value time time

. tab time, mis

    Time/wave |      Freq.     Percent        Cum.
--------------+-----------------------------------
  spring 2020 |      3,278      100.00      100.00
--------------+-----------------------------------
        Total |      3,278      100.00

. 
. *Repair id variable
. tab id, mis

         id |      Freq.     Percent        Cum.
------------+-----------------------------------
         10 |          1        0.03        0.03
         11 |          1        0.03        0.06
         12 |          1        0.03        0.09
         13 |          1        0.03        0.12
         14 |          1        0.03        0.15
         15 |          1        0.03        0.18
         16 |          1        0.03        0.21
         18 |          1        0.03        0.24
         19 |          1        0.03        0.27
         20 |          1        0.03        0.31
         21 |          1        0.03        0.34
         22 |          1        0.03        0.37
         23 |          1        0.03        0.40
         25 |          1        0.03        0.43
         26 |          1        0.03        0.46
         27 |          1        0.03        0.49
         28 |          1        0.03        0.52
         29 |          1        0.03        0.55
         30 |          1        0.03        0.58
         31 |          1        0.03        0.61
         32 |          1        0.03        0.64
         33 |          1        0.03        0.67
         34 |          1        0.03        0.70
         35 |          1        0.03        0.73
         36 |          1        0.03        0.76
         37 |          1        0.03        0.79
         38 |          1        0.03        0.82
         39 |          1        0.03        0.85
         40 |          1        0.03        0.88
         41 |          1        0.03        0.92
         43 |          1        0.03        0.95
         44 |          1        0.03        0.98
         45 |          1        0.03        1.01
         46 |          1        0.03        1.04
         48 |          1        0.03        1.07
         49 |          1        0.03        1.10
         50 |          1        0.03        1.13
         52 |          1        0.03        1.16
         53 |          1        0.03        1.19
         54 |          1        0.03        1.22
         55 |          1        0.03        1.25
         56 |          1        0.03        1.28
         57 |          1        0.03        1.31
         58 |          1        0.03        1.34
         59 |          1        0.03        1.37
         60 |          1        0.03        1.40
         61 |          1        0.03        1.43
         63 |          1        0.03        1.46
         65 |          1        0.03        1.49
         66 |          1        0.03        1.53
         67 |          1        0.03        1.56
         68 |          1        0.03        1.59
         69 |          1        0.03        1.62
         70 |          1        0.03        1.65
         71 |          1        0.03        1.68
         72 |          1        0.03        1.71
         73 |          1        0.03        1.74
         74 |          1        0.03        1.77
         75 |          1        0.03        1.80
         76 |          1        0.03        1.83
         77 |          1        0.03        1.86
         78 |          1        0.03        1.89
         79 |          1        0.03        1.92
         80 |          1        0.03        1.95
         81 |          1        0.03        1.98
         82 |          1        0.03        2.01
         83 |          1        0.03        2.04
         84 |          1        0.03        2.07
         85 |          1        0.03        2.10
         86 |          1        0.03        2.14
         87 |          1        0.03        2.17
         88 |          1        0.03        2.20
         89 |          1        0.03        2.23
         90 |          1        0.03        2.26
         91 |          1        0.03        2.29
         92 |          1        0.03        2.32
         93 |          1        0.03        2.35
         94 |          1        0.03        2.38
         95 |          1        0.03        2.41
         96 |          1        0.03        2.44
         97 |          1        0.03        2.47
         98 |          1        0.03        2.50
        100 |          1        0.03        2.53
        101 |          1        0.03        2.56
        102 |          1        0.03        2.59
        103 |          1        0.03        2.62
        104 |          1        0.03        2.65
        105 |          1        0.03        2.68
        106 |          1        0.03        2.72
        107 |          1        0.03        2.75
        108 |          1        0.03        2.78
        109 |          1        0.03        2.81
        110 |          1        0.03        2.84
        112 |          1        0.03        2.87
        113 |          1        0.03        2.90
        114 |          1        0.03        2.93
        115 |          1        0.03        2.96
        116 |          1        0.03        2.99
        118 |          1        0.03        3.02
        119 |          1        0.03        3.05
        120 |          1        0.03        3.08
        121 |          1        0.03        3.11
        122 |          1        0.03        3.14
        123 |          1        0.03        3.17
        124 |          1        0.03        3.20
        125 |          1        0.03        3.23
        126 |          1        0.03        3.26
        128 |          1        0.03        3.29
        129 |          1        0.03        3.33
        130 |          1        0.03        3.36
        131 |          1        0.03        3.39
        132 |          1        0.03        3.42
        133 |          1        0.03        3.45
        134 |          1        0.03        3.48
        135 |          1        0.03        3.51
        136 |          1        0.03        3.54
        137 |          1        0.03        3.57
        138 |          1        0.03        3.60
        139 |          1        0.03        3.63
        140 |          1        0.03        3.66
        141 |          1        0.03        3.69
        143 |          1        0.03        3.72
        144 |          1        0.03        3.75
        145 |          1        0.03        3.78
        146 |          1        0.03        3.81
        147 |          1        0.03        3.84
        148 |          1        0.03        3.87
        150 |          1        0.03        3.90
        151 |          1        0.03        3.94
        153 |          1        0.03        3.97
        154 |          1        0.03        4.00
        155 |          1        0.03        4.03
        156 |          1        0.03        4.06
        157 |          1        0.03        4.09
        158 |          1        0.03        4.12
        159 |          1        0.03        4.15
        161 |          1        0.03        4.18
        162 |          1        0.03        4.21
        164 |          1        0.03        4.24
        165 |          1        0.03        4.27
        166 |          1        0.03        4.30
        167 |          1        0.03        4.33
        168 |          1        0.03        4.36
        169 |          1        0.03        4.39
        170 |          1        0.03        4.42
        171 |          1        0.03        4.45
        172 |          1        0.03        4.48
        173 |          1        0.03        4.51
        174 |          1        0.03        4.55
        175 |          1        0.03        4.58
        176 |          1        0.03        4.61
        177 |          1        0.03        4.64
        178 |          1        0.03        4.67
        179 |          1        0.03        4.70
        180 |          1        0.03        4.73
        181 |          1        0.03        4.76
        182 |          1        0.03        4.79
        183 |          1        0.03        4.82
        184 |          1        0.03        4.85
        185 |          1        0.03        4.88
        186 |          1        0.03        4.91
        187 |          1        0.03        4.94
        188 |          1        0.03        4.97
        189 |          1        0.03        5.00
        190 |          1        0.03        5.03
        191 |          1        0.03        5.06
        192 |          1        0.03        5.09
        194 |          1        0.03        5.13
        195 |          1        0.03        5.16
        196 |          1        0.03        5.19
        197 |          1        0.03        5.22
        198 |          1        0.03        5.25
        199 |          1        0.03        5.28
        200 |          1        0.03        5.31
        202 |          1        0.03        5.34
        203 |          1        0.03        5.37
        204 |          1        0.03        5.40
        205 |          1        0.03        5.43
        206 |          1        0.03        5.46
        207 |          1        0.03        5.49
        208 |          1        0.03        5.52
        209 |          1        0.03        5.55
        210 |          1        0.03        5.58
        211 |          1        0.03        5.61
        212 |          1        0.03        5.64
        213 |          1        0.03        5.67
        214 |          1        0.03        5.70
        215 |          1        0.03        5.74
        216 |          1        0.03        5.77
        217 |          1        0.03        5.80
        218 |          1        0.03        5.83
        219 |          1        0.03        5.86
        220 |          1        0.03        5.89
        221 |          1        0.03        5.92
        222 |          1        0.03        5.95
        223 |          1        0.03        5.98
        224 |          1        0.03        6.01
        225 |          1        0.03        6.04
        226 |          1        0.03        6.07
        227 |          1        0.03        6.10
        228 |          1        0.03        6.13
        229 |          1        0.03        6.16
        230 |          1        0.03        6.19
        231 |          1        0.03        6.22
        232 |          1        0.03        6.25
        233 |          1        0.03        6.28
        235 |          1        0.03        6.31
        236 |          1        0.03        6.35
        237 |          1        0.03        6.38
        238 |          1        0.03        6.41
        239 |          1        0.03        6.44
        241 |          1        0.03        6.47
        242 |          1        0.03        6.50
        243 |          1        0.03        6.53
        244 |          1        0.03        6.56
        245 |          1        0.03        6.59
        246 |          1        0.03        6.62
        247 |          1        0.03        6.65
        248 |          1        0.03        6.68
        249 |          1        0.03        6.71
        250 |          1        0.03        6.74
        252 |          1        0.03        6.77
        253 |          1        0.03        6.80
        254 |          1        0.03        6.83
        255 |          1        0.03        6.86
        256 |          1        0.03        6.89
        257 |          1        0.03        6.92
        258 |          1        0.03        6.96
        259 |          1        0.03        6.99
        260 |          1        0.03        7.02
        261 |          1        0.03        7.05
        262 |          1        0.03        7.08
        263 |          1        0.03        7.11
        264 |          1        0.03        7.14
        266 |          1        0.03        7.17
        267 |          1        0.03        7.20
        268 |          1        0.03        7.23
        269 |          1        0.03        7.26
        271 |          1        0.03        7.29
        272 |          1        0.03        7.32
        273 |          1        0.03        7.35
        274 |          1        0.03        7.38
        275 |          1        0.03        7.41
        276 |          1        0.03        7.44
        277 |          1        0.03        7.47
        278 |          1        0.03        7.50
        279 |          1        0.03        7.54
        280 |          1        0.03        7.57
        282 |          1        0.03        7.60
        283 |          1        0.03        7.63
        284 |          1        0.03        7.66
        287 |          1        0.03        7.69
        288 |          1        0.03        7.72
        289 |          1        0.03        7.75
        290 |          1        0.03        7.78
        291 |          1        0.03        7.81
        292 |          1        0.03        7.84
        293 |          1        0.03        7.87
        294 |          1        0.03        7.90
        296 |          1        0.03        7.93
        297 |          1        0.03        7.96
        298 |          1        0.03        7.99
        300 |          1        0.03        8.02
        301 |          1        0.03        8.05
        303 |          1        0.03        8.08
        305 |          1        0.03        8.11
        306 |          1        0.03        8.15
        307 |          1        0.03        8.18
        308 |          1        0.03        8.21
        310 |          1        0.03        8.24
        311 |          1        0.03        8.27
        313 |          1        0.03        8.30
        314 |          1        0.03        8.33
        315 |          1        0.03        8.36
        316 |          1        0.03        8.39
        317 |          1        0.03        8.42
        319 |          1        0.03        8.45
        320 |          1        0.03        8.48
        322 |          1        0.03        8.51
        323 |          1        0.03        8.54
        324 |          1        0.03        8.57
        325 |          1        0.03        8.60
        326 |          1        0.03        8.63
        327 |          1        0.03        8.66
        328 |          1        0.03        8.69
        329 |          1        0.03        8.72
        330 |          1        0.03        8.76
        331 |          1        0.03        8.79
        333 |          1        0.03        8.82
        336 |          1        0.03        8.85
        337 |          1        0.03        8.88
        338 |          1        0.03        8.91
        339 |          1        0.03        8.94
        340 |          1        0.03        8.97
        341 |          1        0.03        9.00
        342 |          1        0.03        9.03
        343 |          1        0.03        9.06
        344 |          1        0.03        9.09
        345 |          1        0.03        9.12
        346 |          1        0.03        9.15
        347 |          1        0.03        9.18
        348 |          1        0.03        9.21
        349 |          1        0.03        9.24
        350 |          1        0.03        9.27
        351 |          1        0.03        9.30
        352 |          1        0.03        9.33
        353 |          1        0.03        9.37
        354 |          1        0.03        9.40
        355 |          1        0.03        9.43
        356 |          1        0.03        9.46
        359 |          1        0.03        9.49
        361 |          1        0.03        9.52
        363 |          1        0.03        9.55
        364 |          1        0.03        9.58
        365 |          1        0.03        9.61
        368 |          1        0.03        9.64
        369 |          1        0.03        9.67
        370 |          1        0.03        9.70
        371 |          1        0.03        9.73
        372 |          1        0.03        9.76
        380 |          1        0.03        9.79
        381 |          1        0.03        9.82
        382 |          1        0.03        9.85
        383 |          1        0.03        9.88
        386 |          1        0.03        9.91
        387 |          1        0.03        9.95
        389 |          1        0.03        9.98
        390 |          1        0.03       10.01
        392 |          1        0.03       10.04
        393 |          1        0.03       10.07
        394 |          1        0.03       10.10
        395 |          1        0.03       10.13
        396 |          1        0.03       10.16
        398 |          1        0.03       10.19
        400 |          1        0.03       10.22
        401 |          1        0.03       10.25
        402 |          1        0.03       10.28
        403 |          1        0.03       10.31
        404 |          1        0.03       10.34
        405 |          1        0.03       10.37
        406 |          1        0.03       10.40
        407 |          1        0.03       10.43
        408 |          1        0.03       10.46
        409 |          1        0.03       10.49
        410 |          1        0.03       10.52
        411 |          1        0.03       10.56
        412 |          1        0.03       10.59
        413 |          1        0.03       10.62
        414 |          1        0.03       10.65
        415 |          1        0.03       10.68
        417 |          1        0.03       10.71
        418 |          1        0.03       10.74
        419 |          1        0.03       10.77
        420 |          1        0.03       10.80
        421 |          1        0.03       10.83
        422 |          1        0.03       10.86
        423 |          1        0.03       10.89
        424 |          1        0.03       10.92
        425 |          1        0.03       10.95
        426 |          1        0.03       10.98
        428 |          1        0.03       11.01
        429 |          1        0.03       11.04
        430 |          1        0.03       11.07
        431 |          1        0.03       11.10
        432 |          1        0.03       11.13
        433 |          1        0.03       11.17
        434 |          1        0.03       11.20
        435 |          1        0.03       11.23
        438 |          1        0.03       11.26
        439 |          1        0.03       11.29
        440 |          1        0.03       11.32
        441 |          1        0.03       11.35
        442 |          1        0.03       11.38
        443 |          1        0.03       11.41
        446 |          1        0.03       11.44
        447 |          1        0.03       11.47
        448 |          1        0.03       11.50
        449 |          1        0.03       11.53
        450 |          1        0.03       11.56
        451 |          1        0.03       11.59
        452 |          1        0.03       11.62
        453 |          1        0.03       11.65
        455 |          1        0.03       11.68
        456 |          1        0.03       11.71
        457 |          1        0.03       11.74
        458 |          1        0.03       11.78
        459 |          1        0.03       11.81
        460 |          1        0.03       11.84
        461 |          1        0.03       11.87
        462 |          1        0.03       11.90
        463 |          1        0.03       11.93
        464 |          1        0.03       11.96
        465 |          1        0.03       11.99
        467 |          1        0.03       12.02
        468 |          1        0.03       12.05
        470 |          1        0.03       12.08
        471 |          1        0.03       12.11
        472 |          1        0.03       12.14
        473 |          1        0.03       12.17
        474 |          1        0.03       12.20
        475 |          1        0.03       12.23
        477 |          1        0.03       12.26
        478 |          1        0.03       12.29
        479 |          1        0.03       12.32
        480 |          1        0.03       12.36
        481 |          1        0.03       12.39
        482 |          1        0.03       12.42
        483 |          1        0.03       12.45
        484 |          1        0.03       12.48
        486 |          1        0.03       12.51
        487 |          1        0.03       12.54
        488 |          1        0.03       12.57
        489 |          1        0.03       12.60
        491 |          1        0.03       12.63
        495 |          1        0.03       12.66
        496 |          1        0.03       12.69
        498 |          1        0.03       12.72
        500 |          1        0.03       12.75
        505 |          1        0.03       12.78
        506 |          1        0.03       12.81
        507 |          1        0.03       12.84
        508 |          1        0.03       12.87
        509 |          1        0.03       12.90
        510 |          1        0.03       12.93
        511 |          1        0.03       12.97
        512 |          1        0.03       13.00
        513 |          1        0.03       13.03
        514 |          1        0.03       13.06
        515 |          1        0.03       13.09
        516 |          1        0.03       13.12
        517 |          1        0.03       13.15
        518 |          1        0.03       13.18
        519 |          1        0.03       13.21
        520 |          1        0.03       13.24
        521 |          1        0.03       13.27
        522 |          1        0.03       13.30
        523 |          1        0.03       13.33
        524 |          1        0.03       13.36
        525 |          1        0.03       13.39
        527 |          1        0.03       13.42
        528 |          1        0.03       13.45
        530 |          1        0.03       13.48
        531 |          1        0.03       13.51
        532 |          1        0.03       13.54
        533 |          1        0.03       13.58
        534 |          1        0.03       13.61
        535 |          1        0.03       13.64
        536 |          1        0.03       13.67
        537 |          1        0.03       13.70
        539 |          1        0.03       13.73
        541 |          1        0.03       13.76
        542 |          1        0.03       13.79
        543 |          1        0.03       13.82
        545 |          1        0.03       13.85
        546 |          1        0.03       13.88
        549 |          1        0.03       13.91
        550 |          1        0.03       13.94
        551 |          1        0.03       13.97
        552 |          1        0.03       14.00
        553 |          1        0.03       14.03
        554 |          1        0.03       14.06
        555 |          1        0.03       14.09
        556 |          1        0.03       14.12
        557 |          1        0.03       14.15
        558 |          1        0.03       14.19
        559 |          1        0.03       14.22
        560 |          1        0.03       14.25
        561 |          1        0.03       14.28
        562 |          1        0.03       14.31
        563 |          1        0.03       14.34
        564 |          1        0.03       14.37
        565 |          1        0.03       14.40
        566 |          1        0.03       14.43
        567 |          1        0.03       14.46
        568 |          1        0.03       14.49
        569 |          1        0.03       14.52
        570 |          1        0.03       14.55
        571 |          1        0.03       14.58
        572 |          1        0.03       14.61
        573 |          1        0.03       14.64
        576 |          1        0.03       14.67
        577 |          1        0.03       14.70
        578 |          1        0.03       14.73
        579 |          1        0.03       14.77
        580 |          1        0.03       14.80
        581 |          1        0.03       14.83
        582 |          1        0.03       14.86
        583 |          1        0.03       14.89
        584 |          1        0.03       14.92
        585 |          1        0.03       14.95
        586 |          1        0.03       14.98
        587 |          1        0.03       15.01
        588 |          1        0.03       15.04
        589 |          1        0.03       15.07
        590 |          1        0.03       15.10
        591 |          1        0.03       15.13
        592 |          1        0.03       15.16
        593 |          1        0.03       15.19
        594 |          1        0.03       15.22
        595 |          1        0.03       15.25
        596 |          1        0.03       15.28
        597 |          1        0.03       15.31
        598 |          1        0.03       15.34
        599 |          1        0.03       15.38
        600 |          1        0.03       15.41
        601 |          1        0.03       15.44
        602 |          1        0.03       15.47
        603 |          1        0.03       15.50
        604 |          1        0.03       15.53
        605 |          1        0.03       15.56
        606 |          1        0.03       15.59
        607 |          1        0.03       15.62
        609 |          1        0.03       15.65
        610 |          1        0.03       15.68
        612 |          1        0.03       15.71
        613 |          1        0.03       15.74
        614 |          1        0.03       15.77
        616 |          1        0.03       15.80
        617 |          1        0.03       15.83
        618 |          1        0.03       15.86
        619 |          1        0.03       15.89
        620 |          1        0.03       15.92
        621 |          1        0.03       15.95
        623 |          1        0.03       15.99
        624 |          1        0.03       16.02
        625 |          1        0.03       16.05
        626 |          1        0.03       16.08
        627 |          1        0.03       16.11
        628 |          1        0.03       16.14
        629 |          1        0.03       16.17
        631 |          1        0.03       16.20
        632 |          1        0.03       16.23
        633 |          1        0.03       16.26
        634 |          1        0.03       16.29
        635 |          1        0.03       16.32
        636 |          1        0.03       16.35
        638 |          1        0.03       16.38
        639 |          1        0.03       16.41
        640 |          1        0.03       16.44
        641 |          1        0.03       16.47
        642 |          1        0.03       16.50
        643 |          1        0.03       16.53
        644 |          1        0.03       16.56
        645 |          1        0.03       16.60
        646 |          1        0.03       16.63
        647 |          1        0.03       16.66
        648 |          1        0.03       16.69
        649 |          1        0.03       16.72
        650 |          1        0.03       16.75
        651 |          1        0.03       16.78
        652 |          1        0.03       16.81
        653 |          1        0.03       16.84
        654 |          1        0.03       16.87
        655 |          1        0.03       16.90
        656 |          1        0.03       16.93
        657 |          1        0.03       16.96
        658 |          1        0.03       16.99
        659 |          1        0.03       17.02
        661 |          1        0.03       17.05
        662 |          1        0.03       17.08
        663 |          1        0.03       17.11
        664 |          1        0.03       17.14
        665 |          1        0.03       17.18
        667 |          1        0.03       17.21
        668 |          1        0.03       17.24
        669 |          1        0.03       17.27
        670 |          1        0.03       17.30
        671 |          1        0.03       17.33
        672 |          1        0.03       17.36
        673 |          1        0.03       17.39
        674 |          1        0.03       17.42
        675 |          1        0.03       17.45
        676 |          1        0.03       17.48
        677 |          1        0.03       17.51
        678 |          1        0.03       17.54
        679 |          1        0.03       17.57
        680 |          1        0.03       17.60
        681 |          1        0.03       17.63
        682 |          1        0.03       17.66
        683 |          1        0.03       17.69
        684 |          1        0.03       17.72
        685 |          1        0.03       17.75
        686 |          1        0.03       17.79
        687 |          1        0.03       17.82
        688 |          1        0.03       17.85
        689 |          1        0.03       17.88
        691 |          1        0.03       17.91
        692 |          1        0.03       17.94
        693 |          1        0.03       17.97
        694 |          1        0.03       18.00
        695 |          1        0.03       18.03
        696 |          1        0.03       18.06
        697 |          1        0.03       18.09
        698 |          1        0.03       18.12
        699 |          1        0.03       18.15
        700 |          1        0.03       18.18
        701 |          1        0.03       18.21
        702 |          1        0.03       18.24
        703 |          1        0.03       18.27
        704 |          1        0.03       18.30
        705 |          1        0.03       18.33
        706 |          1        0.03       18.36
        707 |          1        0.03       18.40
        708 |          1        0.03       18.43
        709 |          1        0.03       18.46
        710 |          1        0.03       18.49
        712 |          1        0.03       18.52
        715 |          1        0.03       18.55
        716 |          1        0.03       18.58
        717 |          1        0.03       18.61
        718 |          1        0.03       18.64
        719 |          1        0.03       18.67
        720 |          1        0.03       18.70
        721 |          1        0.03       18.73
        722 |          1        0.03       18.76
        723 |          1        0.03       18.79
        724 |          1        0.03       18.82
        725 |          1        0.03       18.85
        726 |          1        0.03       18.88
        727 |          1        0.03       18.91
        728 |          1        0.03       18.94
        730 |          1        0.03       18.97
        731 |          1        0.03       19.01
        732 |          1        0.03       19.04
        733 |          1        0.03       19.07
        734 |          1        0.03       19.10
        735 |          1        0.03       19.13
        736 |          1        0.03       19.16
        737 |          1        0.03       19.19
        738 |          1        0.03       19.22
        739 |          1        0.03       19.25
        740 |          1        0.03       19.28
        741 |          1        0.03       19.31
        742 |          1        0.03       19.34
        743 |          1        0.03       19.37
        744 |          1        0.03       19.40
        745 |          1        0.03       19.43
        746 |          1        0.03       19.46
        749 |          1        0.03       19.49
        750 |          1        0.03       19.52
        752 |          1        0.03       19.55
        753 |          1        0.03       19.59
        754 |          1        0.03       19.62
        755 |          1        0.03       19.65
        756 |          1        0.03       19.68
        757 |          1        0.03       19.71
        759 |          1        0.03       19.74
        760 |          1        0.03       19.77
        761 |          1        0.03       19.80
        762 |          1        0.03       19.83
        763 |          1        0.03       19.86
        764 |          1        0.03       19.89
        765 |          1        0.03       19.92
        766 |          1        0.03       19.95
        768 |          1        0.03       19.98
        769 |          1        0.03       20.01
        770 |          1        0.03       20.04
        771 |          1        0.03       20.07
        772 |          1        0.03       20.10
        774 |          1        0.03       20.13
        775 |          1        0.03       20.16
        776 |          1        0.03       20.20
        777 |          1        0.03       20.23
        778 |          1        0.03       20.26
        779 |          1        0.03       20.29
        781 |          1        0.03       20.32
        782 |          1        0.03       20.35
        783 |          1        0.03       20.38
        784 |          1        0.03       20.41
        785 |          1        0.03       20.44
        788 |          1        0.03       20.47
        789 |          1        0.03       20.50
        790 |          1        0.03       20.53
        791 |          1        0.03       20.56
        792 |          1        0.03       20.59
        793 |          1        0.03       20.62
        794 |          1        0.03       20.65
        795 |          1        0.03       20.68
        797 |          1        0.03       20.71
        798 |          1        0.03       20.74
        799 |          1        0.03       20.77
        800 |          1        0.03       20.81
        801 |          1        0.03       20.84
        802 |          1        0.03       20.87
        803 |          1        0.03       20.90
        804 |          1        0.03       20.93
        805 |          1        0.03       20.96
        806 |          1        0.03       20.99
        807 |          1        0.03       21.02
        808 |          1        0.03       21.05
        809 |          1        0.03       21.08
        811 |          1        0.03       21.11
        812 |          1        0.03       21.14
        813 |          1        0.03       21.17
        814 |          1        0.03       21.20
        815 |          1        0.03       21.23
        816 |          1        0.03       21.26
        817 |          1        0.03       21.29
        818 |          1        0.03       21.32
        819 |          1        0.03       21.35
        820 |          1        0.03       21.38
        821 |          1        0.03       21.42
        822 |          1        0.03       21.45
        824 |          1        0.03       21.48
        825 |          1        0.03       21.51
        826 |          1        0.03       21.54
        827 |          1        0.03       21.57
        828 |          1        0.03       21.60
        831 |          1        0.03       21.63
        832 |          1        0.03       21.66
        833 |          1        0.03       21.69
        834 |          1        0.03       21.72
        835 |          1        0.03       21.75
        837 |          1        0.03       21.78
        838 |          1        0.03       21.81
        839 |          1        0.03       21.84
        841 |          1        0.03       21.87
        842 |          1        0.03       21.90
        843 |          1        0.03       21.93
        844 |          1        0.03       21.96
        846 |          1        0.03       22.00
        847 |          1        0.03       22.03
        848 |          1        0.03       22.06
        849 |          1        0.03       22.09
        850 |          1        0.03       22.12
        851 |          1        0.03       22.15
        852 |          1        0.03       22.18
        853 |          1        0.03       22.21
        854 |          1        0.03       22.24
        855 |          1        0.03       22.27
        856 |          1        0.03       22.30
        857 |          1        0.03       22.33
        859 |          1        0.03       22.36
        860 |          1        0.03       22.39
        863 |          1        0.03       22.42
        864 |          1        0.03       22.45
        865 |          1        0.03       22.48
        866 |          1        0.03       22.51
        867 |          1        0.03       22.54
        868 |          1        0.03       22.57
        869 |          1        0.03       22.61
        870 |          1        0.03       22.64
        871 |          1        0.03       22.67
        873 |          1        0.03       22.70
        874 |          1        0.03       22.73
        875 |          1        0.03       22.76
        876 |          1        0.03       22.79
        877 |          1        0.03       22.82
        878 |          1        0.03       22.85
        879 |          1        0.03       22.88
        880 |          1        0.03       22.91
        881 |          1        0.03       22.94
        882 |          1        0.03       22.97
        883 |          1        0.03       23.00
        884 |          1        0.03       23.03
        885 |          1        0.03       23.06
        886 |          1        0.03       23.09
        888 |          1        0.03       23.12
        889 |          1        0.03       23.15
        890 |          1        0.03       23.18
        891 |          1        0.03       23.22
        892 |          1        0.03       23.25
        893 |          1        0.03       23.28
        894 |          1        0.03       23.31
        896 |          1        0.03       23.34
        897 |          1        0.03       23.37
        898 |          1        0.03       23.40
        899 |          1        0.03       23.43
        901 |          1        0.03       23.46
        902 |          1        0.03       23.49
        903 |          1        0.03       23.52
        905 |          1        0.03       23.55
        906 |          1        0.03       23.58
        907 |          1        0.03       23.61
        908 |          1        0.03       23.64
        909 |          1        0.03       23.67
        910 |          1        0.03       23.70
        911 |          1        0.03       23.73
        912 |          1        0.03       23.76
        913 |          1        0.03       23.79
        914 |          1        0.03       23.83
        915 |          1        0.03       23.86
        918 |          1        0.03       23.89
        919 |          1        0.03       23.92
        920 |          1        0.03       23.95
        921 |          1        0.03       23.98
        922 |          1        0.03       24.01
        923 |          1        0.03       24.04
        924 |          1        0.03       24.07
        925 |          1        0.03       24.10
        926 |          1        0.03       24.13
        928 |          1        0.03       24.16
        929 |          1        0.03       24.19
        930 |          1        0.03       24.22
        931 |          1        0.03       24.25
        932 |          1        0.03       24.28
        933 |          1        0.03       24.31
        934 |          1        0.03       24.34
        935 |          1        0.03       24.37
        936 |          1        0.03       24.41
        937 |          1        0.03       24.44
        938 |          1        0.03       24.47
        939 |          1        0.03       24.50
        940 |          1        0.03       24.53
        941 |          1        0.03       24.56
        942 |          1        0.03       24.59
        943 |          1        0.03       24.62
        944 |          1        0.03       24.65
        946 |          1        0.03       24.68
        951 |          1        0.03       24.71
        952 |          1        0.03       24.74
        953 |          1        0.03       24.77
        954 |          1        0.03       24.80
        955 |          1        0.03       24.83
        956 |          1        0.03       24.86
        957 |          1        0.03       24.89
        958 |          1        0.03       24.92
        959 |          1        0.03       24.95
        960 |          1        0.03       24.98
        961 |          1        0.03       25.02
        962 |          1        0.03       25.05
        963 |          1        0.03       25.08
        964 |          1        0.03       25.11
        965 |          1        0.03       25.14
        966 |          1        0.03       25.17
        967 |          1        0.03       25.20
        968 |          1        0.03       25.23
        969 |          1        0.03       25.26
        972 |          1        0.03       25.29
        973 |          1        0.03       25.32
        974 |          1        0.03       25.35
        975 |          1        0.03       25.38
        976 |          1        0.03       25.41
        977 |          1        0.03       25.44
        978 |          1        0.03       25.47
        979 |          1        0.03       25.50
        980 |          1        0.03       25.53
        981 |          1        0.03       25.56
        982 |          1        0.03       25.59
        983 |          1        0.03       25.63
        984 |          1        0.03       25.66
        985 |          1        0.03       25.69
        986 |          1        0.03       25.72
        987 |          1        0.03       25.75
        988 |          1        0.03       25.78
        989 |          1        0.03       25.81
        990 |          1        0.03       25.84
        991 |          1        0.03       25.87
        992 |          1        0.03       25.90
        993 |          1        0.03       25.93
        994 |          1        0.03       25.96
        995 |          1        0.03       25.99
        996 |          1        0.03       26.02
        998 |          1        0.03       26.05
        999 |          1        0.03       26.08
       1000 |          1        0.03       26.11
       1001 |          1        0.03       26.14
       1003 |          1        0.03       26.17
       1004 |          1        0.03       26.21
       1005 |          1        0.03       26.24
       1006 |          1        0.03       26.27
       1008 |          1        0.03       26.30
       1009 |          1        0.03       26.33
       1011 |          1        0.03       26.36
       1012 |          1        0.03       26.39
       1013 |          1        0.03       26.42
       1014 |          1        0.03       26.45
       1015 |          1        0.03       26.48
       1016 |          1        0.03       26.51
       1017 |          1        0.03       26.54
       1018 |          1        0.03       26.57
       1020 |          1        0.03       26.60
       1021 |          1        0.03       26.63
       1022 |          1        0.03       26.66
       1023 |          1        0.03       26.69
       1024 |          1        0.03       26.72
       1025 |          1        0.03       26.75
       1027 |          1        0.03       26.78
       1028 |          1        0.03       26.82
       1029 |          1        0.03       26.85
       1030 |          1        0.03       26.88
       1031 |          1        0.03       26.91
       1032 |          1        0.03       26.94
       1033 |          1        0.03       26.97
       1034 |          1        0.03       27.00
       1035 |          1        0.03       27.03
       1036 |          1        0.03       27.06
       1037 |          1        0.03       27.09
       1038 |          1        0.03       27.12
       1039 |          1        0.03       27.15
       1041 |          1        0.03       27.18
       1043 |          1        0.03       27.21
       1044 |          1        0.03       27.24
       1045 |          1        0.03       27.27
       1047 |          1        0.03       27.30
       1048 |          1        0.03       27.33
       1049 |          1        0.03       27.36
       1050 |          1        0.03       27.39
       1051 |          1        0.03       27.43
       1052 |          1        0.03       27.46
       1053 |          1        0.03       27.49
       1054 |          1        0.03       27.52
       1055 |          1        0.03       27.55
       1056 |          1        0.03       27.58
       1057 |          1        0.03       27.61
       1059 |          1        0.03       27.64
       1060 |          1        0.03       27.67
       1061 |          1        0.03       27.70
       1062 |          1        0.03       27.73
       1063 |          1        0.03       27.76
       1064 |          1        0.03       27.79
       1065 |          1        0.03       27.82
       1066 |          1        0.03       27.85
       1067 |          1        0.03       27.88
       1068 |          1        0.03       27.91
       1069 |          1        0.03       27.94
       1070 |          1        0.03       27.97
       1071 |          1        0.03       28.00
       1072 |          1        0.03       28.04
       1074 |          1        0.03       28.07
       1075 |          1        0.03       28.10
       1076 |          1        0.03       28.13
       1077 |          1        0.03       28.16
       1078 |          1        0.03       28.19
       1079 |          1        0.03       28.22
       1080 |          1        0.03       28.25
       1082 |          1        0.03       28.28
       1083 |          1        0.03       28.31
       1084 |          1        0.03       28.34
       1085 |          1        0.03       28.37
       1087 |          1        0.03       28.40
       1089 |          1        0.03       28.43
       1090 |          1        0.03       28.46
       1092 |          1        0.03       28.49
       1093 |          1        0.03       28.52
       1094 |          1        0.03       28.55
       1095 |          1        0.03       28.58
       1096 |          1        0.03       28.62
       1097 |          1        0.03       28.65
       1098 |          1        0.03       28.68
       1099 |          1        0.03       28.71
       1101 |          1        0.03       28.74
       1102 |          1        0.03       28.77
       1104 |          1        0.03       28.80
       1105 |          1        0.03       28.83
       1106 |          1        0.03       28.86
       1107 |          1        0.03       28.89
       1108 |          1        0.03       28.92
       1109 |          1        0.03       28.95
       1110 |          1        0.03       28.98
       1111 |          1        0.03       29.01
       1112 |          1        0.03       29.04
       1114 |          1        0.03       29.07
       1115 |          1        0.03       29.10
       1116 |          1        0.03       29.13
       1119 |          1        0.03       29.16
       1120 |          1        0.03       29.19
       1121 |          1        0.03       29.23
       1123 |          1        0.03       29.26
       1124 |          1        0.03       29.29
       1125 |          1        0.03       29.32
       1126 |          1        0.03       29.35
       1128 |          1        0.03       29.38
       1129 |          1        0.03       29.41
       1131 |          1        0.03       29.44
       1132 |          1        0.03       29.47
       1133 |          1        0.03       29.50
       1135 |          1        0.03       29.53
       1136 |          1        0.03       29.56
       1137 |          1        0.03       29.59
       1138 |          1        0.03       29.62
       1139 |          1        0.03       29.65
       1140 |          1        0.03       29.68
       1141 |          1        0.03       29.71
       1143 |          1        0.03       29.74
       1147 |          1        0.03       29.77
       1154 |          1        0.03       29.80
       1155 |          1        0.03       29.84
       1156 |          1        0.03       29.87
       1157 |          1        0.03       29.90
       1158 |          1        0.03       29.93
       1159 |          1        0.03       29.96
       1161 |          1        0.03       29.99
       1162 |          1        0.03       30.02
       1163 |          1        0.03       30.05
       1164 |          1        0.03       30.08
       1165 |          1        0.03       30.11
       1166 |          1        0.03       30.14
       1167 |          1        0.03       30.17
       1168 |          1        0.03       30.20
       1169 |          1        0.03       30.23
       1170 |          1        0.03       30.26
       1171 |          1        0.03       30.29
       1172 |          1        0.03       30.32
       1173 |          1        0.03       30.35
       1174 |          1        0.03       30.38
       1175 |          1        0.03       30.41
       1176 |          1        0.03       30.45
       1177 |          1        0.03       30.48
       1178 |          1        0.03       30.51
       1179 |          1        0.03       30.54
       1180 |          1        0.03       30.57
       1182 |          1        0.03       30.60
       1183 |          1        0.03       30.63
       1184 |          1        0.03       30.66
       1185 |          1        0.03       30.69
       1186 |          1        0.03       30.72
       1187 |          1        0.03       30.75
       1188 |          1        0.03       30.78
       1189 |          1        0.03       30.81
       1191 |          1        0.03       30.84
       1192 |          1        0.03       30.87
       1195 |          1        0.03       30.90
       1196 |          1        0.03       30.93
       1199 |          1        0.03       30.96
       1200 |          1        0.03       30.99
       1201 |          1        0.03       31.03
       1202 |          1        0.03       31.06
       1203 |          1        0.03       31.09
       1204 |          1        0.03       31.12
       1205 |          1        0.03       31.15
       1206 |          1        0.03       31.18
       1207 |          1        0.03       31.21
       1208 |          1        0.03       31.24
       1209 |          1        0.03       31.27
       1210 |          1        0.03       31.30
       1211 |          1        0.03       31.33
       1212 |          1        0.03       31.36
       1213 |          1        0.03       31.39
       1214 |          1        0.03       31.42
       1215 |          1        0.03       31.45
       1216 |          1        0.03       31.48
       1217 |          1        0.03       31.51
       1218 |          1        0.03       31.54
       1219 |          1        0.03       31.57
       1220 |          1        0.03       31.60
       1222 |          1        0.03       31.64
       1224 |          1        0.03       31.67
       1225 |          1        0.03       31.70
       1226 |          1        0.03       31.73
       1227 |          1        0.03       31.76
       1228 |          1        0.03       31.79
       1229 |          1        0.03       31.82
       1230 |          1        0.03       31.85
       1231 |          1        0.03       31.88
       1232 |          1        0.03       31.91
       1233 |          1        0.03       31.94
       1234 |          1        0.03       31.97
       1235 |          1        0.03       32.00
       1236 |          1        0.03       32.03
       1238 |          1        0.03       32.06
       1240 |          1        0.03       32.09
       1241 |          1        0.03       32.12
       1242 |          1        0.03       32.15
       1243 |          1        0.03       32.18
       1244 |          1        0.03       32.21
       1245 |          1        0.03       32.25
       1246 |          1        0.03       32.28
       1247 |          1        0.03       32.31
       1248 |          1        0.03       32.34
       1249 |          1        0.03       32.37
       1250 |          1        0.03       32.40
       1251 |          1        0.03       32.43
       1252 |          1        0.03       32.46
       1253 |          1        0.03       32.49
       1254 |          1        0.03       32.52
       1255 |          1        0.03       32.55
       1256 |          1        0.03       32.58
       1257 |          1        0.03       32.61
       1258 |          1        0.03       32.64
       1259 |          1        0.03       32.67
       1260 |          1        0.03       32.70
       1261 |          1        0.03       32.73
       1263 |          1        0.03       32.76
       1264 |          1        0.03       32.79
       1265 |          1        0.03       32.82
       1266 |          1        0.03       32.86
       1267 |          1        0.03       32.89
       1268 |          1        0.03       32.92
       1269 |          1        0.03       32.95
       1270 |          1        0.03       32.98
       1271 |          1        0.03       33.01
       1272 |          1        0.03       33.04
       1273 |          1        0.03       33.07
       1274 |          1        0.03       33.10
       1275 |          1        0.03       33.13
       1276 |          1        0.03       33.16
       1277 |          1        0.03       33.19
       1278 |          1        0.03       33.22
       1279 |          1        0.03       33.25
       1280 |          1        0.03       33.28
       1281 |          1        0.03       33.31
       1282 |          1        0.03       33.34
       1283 |          1        0.03       33.37
       1284 |          1        0.03       33.40
       1288 |          1        0.03       33.44
       1289 |          1        0.03       33.47
       1290 |          1        0.03       33.50
       1291 |          1        0.03       33.53
       1292 |          1        0.03       33.56
       1293 |          1        0.03       33.59
       1294 |          1        0.03       33.62
       1295 |          1        0.03       33.65
       1296 |          1        0.03       33.68
       1298 |          1        0.03       33.71
       1299 |          1        0.03       33.74
       1300 |          1        0.03       33.77
       1301 |          1        0.03       33.80
       1303 |          1        0.03       33.83
       1304 |          1        0.03       33.86
       1305 |          1        0.03       33.89
       1306 |          1        0.03       33.92
       1307 |          1        0.03       33.95
       1308 |          1        0.03       33.98
       1309 |          1        0.03       34.01
       1310 |          1        0.03       34.05
       1311 |          1        0.03       34.08
       1312 |          1        0.03       34.11
       1313 |          1        0.03       34.14
       1314 |          1        0.03       34.17
       1315 |          1        0.03       34.20
       1316 |          1        0.03       34.23
       1317 |          1        0.03       34.26
       1318 |          1        0.03       34.29
       1319 |          1        0.03       34.32
       1320 |          1        0.03       34.35
       1321 |          1        0.03       34.38
       1322 |          1        0.03       34.41
       1323 |          1        0.03       34.44
       1324 |          1        0.03       34.47
       1325 |          1        0.03       34.50
       1326 |          1        0.03       34.53
       1327 |          1        0.03       34.56
       1328 |          1        0.03       34.59
       1329 |          1        0.03       34.62
       1330 |          1        0.03       34.66
       1331 |          1        0.03       34.69
       1335 |          1        0.03       34.72
       1336 |          1        0.03       34.75
       1338 |          1        0.03       34.78
       1339 |          1        0.03       34.81
       1340 |          1        0.03       34.84
       1341 |          1        0.03       34.87
       1342 |          1        0.03       34.90
       1343 |          1        0.03       34.93
       1344 |          1        0.03       34.96
       1345 |          1        0.03       34.99
       1346 |          1        0.03       35.02
       1347 |          1        0.03       35.05
       1348 |          1        0.03       35.08
       1349 |          1        0.03       35.11
       1351 |          1        0.03       35.14
       1352 |          1        0.03       35.17
       1353 |          1        0.03       35.20
       1356 |          1        0.03       35.23
       1357 |          1        0.03       35.27
       1358 |          1        0.03       35.30
       1359 |          1        0.03       35.33
       1360 |          1        0.03       35.36
       1361 |          1        0.03       35.39
       1362 |          1        0.03       35.42
       1363 |          1        0.03       35.45
       1364 |          1        0.03       35.48
       1365 |          1        0.03       35.51
       1366 |          1        0.03       35.54
       1368 |          1        0.03       35.57
       1369 |          1        0.03       35.60
       1370 |          1        0.03       35.63
       1371 |          1        0.03       35.66
       1373 |          1        0.03       35.69
       1374 |          1        0.03       35.72
       1375 |          1        0.03       35.75
       1376 |          1        0.03       35.78
       1377 |          1        0.03       35.81
       1378 |          1        0.03       35.85
       1379 |          1        0.03       35.88
       1380 |          1        0.03       35.91
       1381 |          1        0.03       35.94
       1382 |          1        0.03       35.97
       1383 |          1        0.03       36.00
       1384 |          1        0.03       36.03
       1385 |          1        0.03       36.06
       1386 |          1        0.03       36.09
       1387 |          1        0.03       36.12
       1388 |          1        0.03       36.15
       1389 |          1        0.03       36.18
       1390 |          1        0.03       36.21
       1392 |          1        0.03       36.24
       1394 |          1        0.03       36.27
       1395 |          1        0.03       36.30
       1396 |          1        0.03       36.33
       1397 |          1        0.03       36.36
       1398 |          1        0.03       36.39
       1399 |          1        0.03       36.42
       1400 |          1        0.03       36.46
       1403 |          1        0.03       36.49
       1404 |          1        0.03       36.52
       1405 |          1        0.03       36.55
       1406 |          1        0.03       36.58
       1407 |          1        0.03       36.61
       1408 |          1        0.03       36.64
       1410 |          1        0.03       36.67
       1411 |          1        0.03       36.70
       1413 |          1        0.03       36.73
       1415 |          1        0.03       36.76
       1416 |          1        0.03       36.79
       1418 |          1        0.03       36.82
       1419 |          1        0.03       36.85
       1420 |          1        0.03       36.88
       1421 |          1        0.03       36.91
       1422 |          1        0.03       36.94
       1423 |          1        0.03       36.97
       1424 |          1        0.03       37.00
       1425 |          1        0.03       37.03
       1426 |          1        0.03       37.07
       1427 |          1        0.03       37.10
       1428 |          1        0.03       37.13
       1429 |          1        0.03       37.16
       1430 |          1        0.03       37.19
       1431 |          1        0.03       37.22
       1432 |          1        0.03       37.25
       1433 |          1        0.03       37.28
       1434 |          1        0.03       37.31
       1435 |          1        0.03       37.34
       1436 |          1        0.03       37.37
       1437 |          1        0.03       37.40
       1438 |          1        0.03       37.43
       1439 |          1        0.03       37.46
       1440 |          1        0.03       37.49
       1441 |          1        0.03       37.52
       1442 |          1        0.03       37.55
       1444 |          1        0.03       37.58
       1445 |          1        0.03       37.61
       1446 |          1        0.03       37.64
       1447 |          1        0.03       37.68
       1448 |          1        0.03       37.71
       1449 |          1        0.03       37.74
       1450 |          1        0.03       37.77
       1451 |          1        0.03       37.80
       1452 |          1        0.03       37.83
       1453 |          1        0.03       37.86
       1454 |          1        0.03       37.89
       1455 |          1        0.03       37.92
       1456 |          1        0.03       37.95
       1458 |          1        0.03       37.98
       1461 |          1        0.03       38.01
       1462 |          1        0.03       38.04
       1463 |          1        0.03       38.07
       1464 |          1        0.03       38.10
       1465 |          1        0.03       38.13
       1466 |          1        0.03       38.16
       1467 |          1        0.03       38.19
       1469 |          1        0.03       38.22
       1471 |          1        0.03       38.26
       1472 |          1        0.03       38.29
       1473 |          1        0.03       38.32
       1474 |          1        0.03       38.35
       1475 |          1        0.03       38.38
       1476 |          1        0.03       38.41
       1477 |          1        0.03       38.44
       1478 |          1        0.03       38.47
       1480 |          1        0.03       38.50
       1481 |          1        0.03       38.53
       1485 |          1        0.03       38.56
       1486 |          1        0.03       38.59
       1487 |          1        0.03       38.62
       1488 |          1        0.03       38.65
       1489 |          1        0.03       38.68
       1490 |          1        0.03       38.71
       1491 |          1        0.03       38.74
       1493 |          1        0.03       38.77
       1495 |          1        0.03       38.80
       1497 |          1        0.03       38.83
       1498 |          1        0.03       38.87
       1499 |          1        0.03       38.90
       1500 |          1        0.03       38.93
       1501 |          1        0.03       38.96
       1502 |          1        0.03       38.99
       1503 |          1        0.03       39.02
       1504 |          1        0.03       39.05
       1505 |          1        0.03       39.08
       1506 |          1        0.03       39.11
       1507 |          1        0.03       39.14
       1508 |          1        0.03       39.17
       1509 |          1        0.03       39.20
       1512 |          1        0.03       39.23
       1514 |          1        0.03       39.26
       1515 |          1        0.03       39.29
       1516 |          1        0.03       39.32
       1517 |          1        0.03       39.35
       1518 |          1        0.03       39.38
       1519 |          1        0.03       39.41
       1521 |          1        0.03       39.44
       1523 |          1        0.03       39.48
       1524 |          1        0.03       39.51
       1525 |          1        0.03       39.54
       1526 |          1        0.03       39.57
       1528 |          1        0.03       39.60
       1529 |          1        0.03       39.63
       1530 |          1        0.03       39.66
       1531 |          1        0.03       39.69
       1532 |          1        0.03       39.72
       1533 |          1        0.03       39.75
       1534 |          1        0.03       39.78
       1535 |          1        0.03       39.81
       1537 |          1        0.03       39.84
       1538 |          1        0.03       39.87
       1539 |          1        0.03       39.90
       1540 |          1        0.03       39.93
       1542 |          1        0.03       39.96
       1543 |          1        0.03       39.99
       1544 |          1        0.03       40.02
       1545 |          1        0.03       40.05
       1546 |          1        0.03       40.09
       1548 |          1        0.03       40.12
       1549 |          1        0.03       40.15
       1550 |          1        0.03       40.18
       1551 |          1        0.03       40.21
       1552 |          1        0.03       40.24
       1554 |          1        0.03       40.27
       1555 |          1        0.03       40.30
       1556 |          1        0.03       40.33
       1557 |          1        0.03       40.36
       1559 |          1        0.03       40.39
       1560 |          1        0.03       40.42
       1561 |          1        0.03       40.45
       1562 |          1        0.03       40.48
       1563 |          1        0.03       40.51
       1564 |          1        0.03       40.54
       1565 |          1        0.03       40.57
       1567 |          1        0.03       40.60
       1569 |          1        0.03       40.63
       1572 |          1        0.03       40.67
       1573 |          1        0.03       40.70
       1574 |          1        0.03       40.73
       1575 |          1        0.03       40.76
       1576 |          1        0.03       40.79
       1577 |          1        0.03       40.82
       1578 |          1        0.03       40.85
       1580 |          1        0.03       40.88
       1581 |          1        0.03       40.91
       1582 |          1        0.03       40.94
       1583 |          1        0.03       40.97
       1585 |          1        0.03       41.00
       1587 |          1        0.03       41.03
       1588 |          1        0.03       41.06
       1597 |          1        0.03       41.09
       1598 |          1        0.03       41.12
       1602 |          1        0.03       41.15
       1605 |          1        0.03       41.18
       1606 |          1        0.03       41.21
       1607 |          1        0.03       41.24
       1608 |          1        0.03       41.28
       1611 |          1        0.03       41.31
       1612 |          1        0.03       41.34
       1613 |          1        0.03       41.37
       1615 |          1        0.03       41.40
       1616 |          1        0.03       41.43
       1620 |          1        0.03       41.46
       1621 |          1        0.03       41.49
       1622 |          1        0.03       41.52
       1623 |          1        0.03       41.55
       1625 |          1        0.03       41.58
       1626 |          1        0.03       41.61
       1627 |          1        0.03       41.64
       1628 |          1        0.03       41.67
       1630 |          1        0.03       41.70
       1633 |          1        0.03       41.73
       1634 |          1        0.03       41.76
       1635 |          1        0.03       41.79
       1637 |          1        0.03       41.82
       1638 |          1        0.03       41.85
       1640 |          1        0.03       41.89
       1641 |          1        0.03       41.92
       1642 |          1        0.03       41.95
       1643 |          1        0.03       41.98
       1644 |          1        0.03       42.01
       1645 |          1        0.03       42.04
       1648 |          1        0.03       42.07
       1649 |          1        0.03       42.10
       1650 |          1        0.03       42.13
       1651 |          1        0.03       42.16
       1652 |          1        0.03       42.19
       1653 |          1        0.03       42.22
       1656 |          1        0.03       42.25
       1657 |          1        0.03       42.28
       1659 |          1        0.03       42.31
       1660 |          1        0.03       42.34
       1662 |          1        0.03       42.37
       1663 |          1        0.03       42.40
       1664 |          1        0.03       42.43
       1665 |          1        0.03       42.46
       1669 |          1        0.03       42.50
       1670 |          1        0.03       42.53
       1671 |          1        0.03       42.56
       1672 |          1        0.03       42.59
       1673 |          1        0.03       42.62
       1674 |          1        0.03       42.65
       1675 |          1        0.03       42.68
       1676 |          1        0.03       42.71
       1678 |          1        0.03       42.74
       1679 |          1        0.03       42.77
       1680 |          1        0.03       42.80
       1682 |          1        0.03       42.83
       1685 |          1        0.03       42.86
       1687 |          1        0.03       42.89
       1688 |          1        0.03       42.92
       1689 |          1        0.03       42.95
       1690 |          1        0.03       42.98
       1691 |          1        0.03       43.01
       1692 |          1        0.03       43.04
       1693 |          1        0.03       43.08
       1694 |          1        0.03       43.11
       1695 |          1        0.03       43.14
       1696 |          1        0.03       43.17
       1697 |          1        0.03       43.20
       1698 |          1        0.03       43.23
       1700 |          1        0.03       43.26
       1702 |          1        0.03       43.29
       1704 |          1        0.03       43.32
       1705 |          1        0.03       43.35
       1706 |          1        0.03       43.38
       1707 |          1        0.03       43.41
       1708 |          1        0.03       43.44
       1709 |          1        0.03       43.47
       1712 |          1        0.03       43.50
       1713 |          1        0.03       43.53
       1714 |          1        0.03       43.56
       1715 |          1        0.03       43.59
       1716 |          1        0.03       43.62
       1717 |          1        0.03       43.65
       1718 |          1        0.03       43.69
       1719 |          1        0.03       43.72
       1720 |          1        0.03       43.75
       1721 |          1        0.03       43.78
       1722 |          1        0.03       43.81
       1725 |          1        0.03       43.84
       1726 |          1        0.03       43.87
       1727 |          1        0.03       43.90
       1728 |          1        0.03       43.93
       1730 |          1        0.03       43.96
       1732 |          1        0.03       43.99
       1733 |          1        0.03       44.02
       1735 |          1        0.03       44.05
       1736 |          1        0.03       44.08
       1738 |          1        0.03       44.11
       1742 |          1        0.03       44.14
       1743 |          1        0.03       44.17
       1744 |          1        0.03       44.20
       1745 |          1        0.03       44.23
       1746 |          1        0.03       44.26
       1751 |          1        0.03       44.30
       1753 |          1        0.03       44.33
       1754 |          1        0.03       44.36
       1756 |          1        0.03       44.39
       1758 |          1        0.03       44.42
       1759 |          1        0.03       44.45
       1760 |          1        0.03       44.48
       1761 |          1        0.03       44.51
       1764 |          1        0.03       44.54
       1767 |          1        0.03       44.57
       1768 |          1        0.03       44.60
       1770 |          1        0.03       44.63
       1771 |          1        0.03       44.66
       1772 |          1        0.03       44.69
       1773 |          1        0.03       44.72
       1775 |          1        0.03       44.75
       1776 |          1        0.03       44.78
       1777 |          1        0.03       44.81
       1779 |          1        0.03       44.84
       1780 |          1        0.03       44.87
       1782 |          1        0.03       44.91
       1786 |          1        0.03       44.94
       1789 |          1        0.03       44.97
       1790 |          1        0.03       45.00
       1791 |          1        0.03       45.03
       1792 |          1        0.03       45.06
       1794 |          1        0.03       45.09
       1796 |          1        0.03       45.12
       1797 |          1        0.03       45.15
       1799 |          1        0.03       45.18
       1801 |          1        0.03       45.21
       1802 |          1        0.03       45.24
       1803 |          1        0.03       45.27
       1805 |          1        0.03       45.30
       1806 |          1        0.03       45.33
       1809 |          1        0.03       45.36
       1810 |          1        0.03       45.39
       1814 |          1        0.03       45.42
       1818 |          1        0.03       45.45
       1821 |          1        0.03       45.49
       1822 |          1        0.03       45.52
       1823 |          1        0.03       45.55
       1825 |          1        0.03       45.58
       1826 |          1        0.03       45.61
       1827 |          1        0.03       45.64
       1829 |          1        0.03       45.67
       1831 |          1        0.03       45.70
       1832 |          1        0.03       45.73
       1833 |          1        0.03       45.76
       1834 |          1        0.03       45.79
       1837 |          1        0.03       45.82
       1838 |          1        0.03       45.85
       1839 |          1        0.03       45.88
       1840 |          1        0.03       45.91
       1841 |          1        0.03       45.94
       1843 |          1        0.03       45.97
       1844 |          1        0.03       46.00
       1845 |          1        0.03       46.03
       1847 |          1        0.03       46.06
       1848 |          1        0.03       46.10
       1849 |          1        0.03       46.13
       1851 |          1        0.03       46.16
       1853 |          1        0.03       46.19
       1854 |          1        0.03       46.22
       1857 |          1        0.03       46.25
       1858 |          1        0.03       46.28
       1861 |          1        0.03       46.31
       1862 |          1        0.03       46.34
       1863 |          1        0.03       46.37
       1864 |          1        0.03       46.40
       1865 |          1        0.03       46.43
       1866 |          1        0.03       46.46
       1867 |          1        0.03       46.49
       1868 |          1        0.03       46.52
       1871 |          1        0.03       46.55
       1872 |          1        0.03       46.58
       1873 |          1        0.03       46.61
       1874 |          1        0.03       46.64
       1875 |          1        0.03       46.67
       1878 |          1        0.03       46.71
       1880 |          1        0.03       46.74
       1883 |          1        0.03       46.77
       1884 |          1        0.03       46.80
       1885 |          1        0.03       46.83
       1886 |          1        0.03       46.86
       1888 |          1        0.03       46.89
       1889 |          1        0.03       46.92
       1890 |          1        0.03       46.95
       1891 |          1        0.03       46.98
       1892 |          1        0.03       47.01
       1893 |          1        0.03       47.04
       1894 |          1        0.03       47.07
       1895 |          1        0.03       47.10
       1897 |          1        0.03       47.13
       1898 |          1        0.03       47.16
       1899 |          1        0.03       47.19
       1900 |          1        0.03       47.22
       1901 |          1        0.03       47.25
       1902 |          1        0.03       47.28
       1903 |          1        0.03       47.32
       1904 |          1        0.03       47.35
       1905 |          1        0.03       47.38
       1906 |          1        0.03       47.41
       1907 |          1        0.03       47.44
       1909 |          1        0.03       47.47
       1911 |          1        0.03       47.50
       1913 |          1        0.03       47.53
       1914 |          1        0.03       47.56
       1915 |          1        0.03       47.59
       1916 |          1        0.03       47.62
       1917 |          1        0.03       47.65
       1918 |          1        0.03       47.68
       1919 |          1        0.03       47.71
       1921 |          1        0.03       47.74
       1922 |          1        0.03       47.77
       1924 |          1        0.03       47.80
       1925 |          1        0.03       47.83
       1927 |          1        0.03       47.86
       1930 |          1        0.03       47.90
       1931 |          1        0.03       47.93
       1933 |          1        0.03       47.96
       1934 |          1        0.03       47.99
       1935 |          1        0.03       48.02
       1936 |          1        0.03       48.05
       1937 |          1        0.03       48.08
       1939 |          1        0.03       48.11
       1940 |          1        0.03       48.14
       1941 |          1        0.03       48.17
       1942 |          1        0.03       48.20
       1943 |          1        0.03       48.23
       1944 |          1        0.03       48.26
       1945 |          1        0.03       48.29
       1946 |          1        0.03       48.32
       1947 |          1        0.03       48.35
       1948 |          1        0.03       48.38
       1949 |          1        0.03       48.41
       1950 |          1        0.03       48.44
       1951 |          1        0.03       48.47
       1953 |          1        0.03       48.51
       1954 |          1        0.03       48.54
       1955 |          1        0.03       48.57
       1956 |          1        0.03       48.60
       1957 |          1        0.03       48.63
       1958 |          1        0.03       48.66
       1959 |          1        0.03       48.69
       1960 |          1        0.03       48.72
       1961 |          1        0.03       48.75
       1962 |          1        0.03       48.78
       1963 |          1        0.03       48.81
       1964 |          1        0.03       48.84
       1965 |          1        0.03       48.87
       1966 |          1        0.03       48.90
       1967 |          1        0.03       48.93
       1968 |          1        0.03       48.96
       1969 |          1        0.03       48.99
       1970 |          1        0.03       49.02
       1971 |          1        0.03       49.05
       1972 |          1        0.03       49.08
       1973 |          1        0.03       49.12
       1974 |          1        0.03       49.15
       1976 |          1        0.03       49.18
       1977 |          1        0.03       49.21
       1978 |          1        0.03       49.24
       1979 |          1        0.03       49.27
       1980 |          1        0.03       49.30
       1982 |          1        0.03       49.33
       1983 |          1        0.03       49.36
       1984 |          1        0.03       49.39
       1985 |          1        0.03       49.42
       1986 |          1        0.03       49.45
       1987 |          1        0.03       49.48
       1988 |          1        0.03       49.51
       1989 |          1        0.03       49.54
       1990 |          1        0.03       49.57
       1991 |          1        0.03       49.60
       1992 |          1        0.03       49.63
       1993 |          1        0.03       49.66
       1994 |          1        0.03       49.69
       1995 |          1        0.03       49.73
       1996 |          1        0.03       49.76
       1997 |          1        0.03       49.79
       1998 |          1        0.03       49.82
       1999 |          1        0.03       49.85
       2000 |          1        0.03       49.88
       2002 |          1        0.03       49.91
       2003 |          1        0.03       49.94
       2004 |          1        0.03       49.97
       2005 |          1        0.03       50.00
       2006 |          1        0.03       50.03
       2007 |          1        0.03       50.06
       2008 |          1        0.03       50.09
       2010 |          1        0.03       50.12
       2011 |          1        0.03       50.15
       2012 |          1        0.03       50.18
       2013 |          1        0.03       50.21
       2014 |          1        0.03       50.24
       2015 |          1        0.03       50.27
       2017 |          1        0.03       50.31
       2018 |          1        0.03       50.34
       2019 |          1        0.03       50.37
       2020 |          1        0.03       50.40
       2021 |          1        0.03       50.43
       2022 |          1        0.03       50.46
       2023 |          1        0.03       50.49
       2024 |          1        0.03       50.52
       2025 |          1        0.03       50.55
       2026 |          1        0.03       50.58
       2027 |          1        0.03       50.61
       2028 |          1        0.03       50.64
       2029 |          1        0.03       50.67
       2030 |          1        0.03       50.70
       2031 |          1        0.03       50.73
       2032 |          1        0.03       50.76
       2034 |          1        0.03       50.79
       2036 |          1        0.03       50.82
       2037 |          1        0.03       50.85
       2038 |          1        0.03       50.88
       2039 |          1        0.03       50.92
       2040 |          1        0.03       50.95
       2041 |          1        0.03       50.98
       2042 |          1        0.03       51.01
       2043 |          1        0.03       51.04
       2044 |          1        0.03       51.07
       2045 |          1        0.03       51.10
       2046 |          1        0.03       51.13
       2047 |          1        0.03       51.16
       2048 |          1        0.03       51.19
       2049 |          1        0.03       51.22
       2050 |          1        0.03       51.25
       2051 |          1        0.03       51.28
       2052 |          1        0.03       51.31
       2053 |          1        0.03       51.34
       2054 |          1        0.03       51.37
       2055 |          1        0.03       51.40
       2056 |          1        0.03       51.43
       2059 |          1        0.03       51.46
       2060 |          1        0.03       51.49
       2061 |          1        0.03       51.53
       2062 |          1        0.03       51.56
       2063 |          1        0.03       51.59
       2064 |          1        0.03       51.62
       2065 |          1        0.03       51.65
       2066 |          1        0.03       51.68
       2067 |          1        0.03       51.71
       2068 |          1        0.03       51.74
       2069 |          1        0.03       51.77
       2070 |          1        0.03       51.80
       2071 |          1        0.03       51.83
       2073 |          1        0.03       51.86
       2074 |          1        0.03       51.89
       2075 |          1        0.03       51.92
       2076 |          1        0.03       51.95
       2077 |          1        0.03       51.98
       2078 |          1        0.03       52.01
       2079 |          1        0.03       52.04
       2080 |          1        0.03       52.07
       2081 |          1        0.03       52.10
       2082 |          1        0.03       52.14
       2083 |          1        0.03       52.17
       2084 |          1        0.03       52.20
       2085 |          1        0.03       52.23
       2087 |          1        0.03       52.26
       2088 |          1        0.03       52.29
       2089 |          1        0.03       52.32
       2090 |          1        0.03       52.35
       2091 |          1        0.03       52.38
       2092 |          1        0.03       52.41
       2094 |          1        0.03       52.44
       2095 |          1        0.03       52.47
       2097 |          1        0.03       52.50
       2098 |          1        0.03       52.53
       2099 |          1        0.03       52.56
       2100 |          1        0.03       52.59
       2101 |          1        0.03       52.62
       2103 |          1        0.03       52.65
       2104 |          1        0.03       52.68
       2105 |          1        0.03       52.72
       2106 |          1        0.03       52.75
       2107 |          1        0.03       52.78
       2108 |          1        0.03       52.81
       2109 |          1        0.03       52.84
       2110 |          1        0.03       52.87
       2111 |          1        0.03       52.90
       2112 |          1        0.03       52.93
       2113 |          1        0.03       52.96
       2114 |          1        0.03       52.99
       2115 |          1        0.03       53.02
       2116 |          1        0.03       53.05
       2117 |          1        0.03       53.08
       2119 |          1        0.03       53.11
       2120 |          1        0.03       53.14
       2121 |          1        0.03       53.17
       2122 |          1        0.03       53.20
       2123 |          1        0.03       53.23
       2124 |          1        0.03       53.26
       2125 |          1        0.03       53.29
       2126 |          1        0.03       53.33
       2127 |          1        0.03       53.36
       2128 |          1        0.03       53.39
       2129 |          1        0.03       53.42
       2130 |          1        0.03       53.45
       2131 |          1        0.03       53.48
       2132 |          1        0.03       53.51
       2133 |          1        0.03       53.54
       2134 |          1        0.03       53.57
       2135 |          1        0.03       53.60
       2136 |          1        0.03       53.63
       2137 |          1        0.03       53.66
       2138 |          1        0.03       53.69
       2139 |          1        0.03       53.72
       2140 |          1        0.03       53.75
       2142 |          1        0.03       53.78
       2143 |          1        0.03       53.81
       2144 |          1        0.03       53.84
       2145 |          1        0.03       53.87
       2147 |          1        0.03       53.90
       2148 |          1        0.03       53.94
       2149 |          1        0.03       53.97
       2150 |          1        0.03       54.00
       2151 |          1        0.03       54.03
       2152 |          1        0.03       54.06
       2153 |          1        0.03       54.09
       2154 |          1        0.03       54.12
       2155 |          1        0.03       54.15
       2157 |          1        0.03       54.18
       2158 |          1        0.03       54.21
       2159 |          1        0.03       54.24
       2160 |          1        0.03       54.27
       2161 |          1        0.03       54.30
       2162 |          1        0.03       54.33
       2165 |          1        0.03       54.36
       2166 |          1        0.03       54.39
       2167 |          1        0.03       54.42
       2168 |          1        0.03       54.45
       2169 |          1        0.03       54.48
       2170 |          1        0.03       54.51
       2171 |          1        0.03       54.55
       2172 |          1        0.03       54.58
       2173 |          1        0.03       54.61
       2175 |          1        0.03       54.64
       2177 |          1        0.03       54.67
       2178 |          1        0.03       54.70
       2179 |          1        0.03       54.73
       2180 |          1        0.03       54.76
       2181 |          1        0.03       54.79
       2183 |          1        0.03       54.82
       2184 |          1        0.03       54.85
       2185 |          1        0.03       54.88
       2186 |          1        0.03       54.91
       2187 |          1        0.03       54.94
       2188 |          1        0.03       54.97
       2189 |          1        0.03       55.00
       2190 |          1        0.03       55.03
       2191 |          1        0.03       55.06
       2192 |          1        0.03       55.09
       2193 |          1        0.03       55.13
       2194 |          1        0.03       55.16
       2195 |          1        0.03       55.19
       2196 |          1        0.03       55.22
       2197 |          1        0.03       55.25
       2198 |          1        0.03       55.28
       2199 |          1        0.03       55.31
       2200 |          1        0.03       55.34
       2201 |          1        0.03       55.37
       2202 |          1        0.03       55.40
       2204 |          1        0.03       55.43
       2206 |          1        0.03       55.46
       2207 |          1        0.03       55.49
       2208 |          1        0.03       55.52
       2209 |          1        0.03       55.55
       2211 |          1        0.03       55.58
       2212 |          1        0.03       55.61
       2214 |          1        0.03       55.64
       2215 |          1        0.03       55.67
       2216 |          1        0.03       55.70
       2218 |          1        0.03       55.74
       2219 |          1        0.03       55.77
       2220 |          1        0.03       55.80
       2221 |          1        0.03       55.83
       2222 |          1        0.03       55.86
       2223 |          1        0.03       55.89
       2224 |          1        0.03       55.92
       2226 |          1        0.03       55.95
       2227 |          1        0.03       55.98
       2228 |          1        0.03       56.01
       2229 |          1        0.03       56.04
       2230 |          1        0.03       56.07
       2231 |          1        0.03       56.10
       2233 |          1        0.03       56.13
       2237 |          1        0.03       56.16
       2238 |          1        0.03       56.19
       2239 |          1        0.03       56.22
       2240 |          1        0.03       56.25
       2241 |          1        0.03       56.28
       2242 |          1        0.03       56.31
       2243 |          1        0.03       56.35
       2244 |          1        0.03       56.38
       2245 |          1        0.03       56.41
       2246 |          1        0.03       56.44
       2247 |          1        0.03       56.47
       2248 |          1        0.03       56.50
       2249 |          1        0.03       56.53
       2250 |          1        0.03       56.56
       2251 |          1        0.03       56.59
       2252 |          1        0.03       56.62
       2254 |          1        0.03       56.65
       2255 |          1        0.03       56.68
       2256 |          1        0.03       56.71
       2259 |          1        0.03       56.74
       2261 |          1        0.03       56.77
       2262 |          1        0.03       56.80
       2263 |          1        0.03       56.83
       2265 |          1        0.03       56.86
       2266 |          1        0.03       56.89
       2267 |          1        0.03       56.92
       2269 |          1        0.03       56.96
       2270 |          1        0.03       56.99
       2271 |          1        0.03       57.02
       2272 |          1        0.03       57.05
       2273 |          1        0.03       57.08
       2274 |          1        0.03       57.11
       2275 |          1        0.03       57.14
       2276 |          1        0.03       57.17
       2277 |          1        0.03       57.20
       2278 |          1        0.03       57.23
       2279 |          1        0.03       57.26
       2280 |          1        0.03       57.29
       2281 |          1        0.03       57.32
       2282 |          1        0.03       57.35
       2283 |          1        0.03       57.38
       2284 |          1        0.03       57.41
       2285 |          1        0.03       57.44
       2287 |          1        0.03       57.47
       2289 |          1        0.03       57.50
       2290 |          1        0.03       57.54
       2291 |          1        0.03       57.57
       2292 |          1        0.03       57.60
       2293 |          1        0.03       57.63
       2295 |          1        0.03       57.66
       2297 |          1        0.03       57.69
       2298 |          1        0.03       57.72
       2299 |          1        0.03       57.75
       2300 |          1        0.03       57.78
       2301 |          1        0.03       57.81
       2302 |          1        0.03       57.84
       2304 |          1        0.03       57.87
       2306 |          1        0.03       57.90
       2308 |          1        0.03       57.93
       2309 |          1        0.03       57.96
       2312 |          1        0.03       57.99
       2313 |          1        0.03       58.02
       2314 |          1        0.03       58.05
       2315 |          1        0.03       58.08
       2316 |          1        0.03       58.11
       2317 |          1        0.03       58.15
       2319 |          1        0.03       58.18
       2320 |          1        0.03       58.21
       2321 |          1        0.03       58.24
       2322 |          1        0.03       58.27
       2323 |          1        0.03       58.30
       2324 |          1        0.03       58.33
       2327 |          1        0.03       58.36
       2331 |          1        0.03       58.39
       2332 |          1        0.03       58.42
       2333 |          1        0.03       58.45
       2334 |          1        0.03       58.48
       2335 |          1        0.03       58.51
       2336 |          1        0.03       58.54
       2337 |          1        0.03       58.57
       2338 |          1        0.03       58.60
       2339 |          1        0.03       58.63
       2340 |          1        0.03       58.66
       2341 |          1        0.03       58.69
       2343 |          1        0.03       58.72
       2344 |          1        0.03       58.76
       2345 |          1        0.03       58.79
       2346 |          1        0.03       58.82
       2347 |          1        0.03       58.85
       2349 |          1        0.03       58.88
       2350 |          1        0.03       58.91
       2351 |          1        0.03       58.94
       2352 |          1        0.03       58.97
       2353 |          1        0.03       59.00
       2354 |          1        0.03       59.03
       2355 |          1        0.03       59.06
       2356 |          1        0.03       59.09
       2357 |          1        0.03       59.12
       2359 |          1        0.03       59.15
       2360 |          1        0.03       59.18
       2362 |          1        0.03       59.21
       2363 |          1        0.03       59.24
       2364 |          1        0.03       59.27
       2365 |          1        0.03       59.30
       2366 |          1        0.03       59.33
       2368 |          1        0.03       59.37
       2369 |          1        0.03       59.40
       2370 |          1        0.03       59.43
       2371 |          1        0.03       59.46
       2372 |          1        0.03       59.49
       2373 |          1        0.03       59.52
       2374 |          1        0.03       59.55
       2375 |          1        0.03       59.58
       2376 |          1        0.03       59.61
       2377 |          1        0.03       59.64
       2379 |          1        0.03       59.67
       2380 |          1        0.03       59.70
       2381 |          1        0.03       59.73
       2382 |          1        0.03       59.76
       2383 |          1        0.03       59.79
       2384 |          1        0.03       59.82
       2385 |          1        0.03       59.85
       2386 |          1        0.03       59.88
       2387 |          1        0.03       59.91
       2388 |          1        0.03       59.95
       2389 |          1        0.03       59.98
       2391 |          1        0.03       60.01
       2392 |          1        0.03       60.04
       2393 |          1        0.03       60.07
       2394 |          1        0.03       60.10
       2395 |          1        0.03       60.13
       2397 |          1        0.03       60.16
       2400 |          1        0.03       60.19
       2401 |          1        0.03       60.22
       2402 |          1        0.03       60.25
       2403 |          1        0.03       60.28
       2404 |          1        0.03       60.31
       2407 |          1        0.03       60.34
       2408 |          1        0.03       60.37
       2409 |          1        0.03       60.40
       2410 |          1        0.03       60.43
       2411 |          1        0.03       60.46
       2412 |          1        0.03       60.49
       2413 |          1        0.03       60.52
       2414 |          1        0.03       60.56
       2415 |          1        0.03       60.59
       2416 |          1        0.03       60.62
       2417 |          1        0.03       60.65
       2418 |          1        0.03       60.68
       2419 |          1        0.03       60.71
       2421 |          1        0.03       60.74
       2422 |          1        0.03       60.77
       2423 |          1        0.03       60.80
       2424 |          1        0.03       60.83
       2425 |          1        0.03       60.86
       2426 |          1        0.03       60.89
       2430 |          1        0.03       60.92
       2432 |          1        0.03       60.95
       2433 |          1        0.03       60.98
       2434 |          1        0.03       61.01
       2435 |          1        0.03       61.04
       2436 |          1        0.03       61.07
       2437 |          1        0.03       61.10
       2439 |          1        0.03       61.13
       2440 |          1        0.03       61.17
       2441 |          1        0.03       61.20
       2442 |          1        0.03       61.23
       2444 |          1        0.03       61.26
       2445 |          1        0.03       61.29
       2446 |          1        0.03       61.32
       2447 |          1        0.03       61.35
       2448 |          1        0.03       61.38
       2449 |          1        0.03       61.41
       2450 |          1        0.03       61.44
       2451 |          1        0.03       61.47
       2452 |          1        0.03       61.50
       2453 |          1        0.03       61.53
       2454 |          1        0.03       61.56
       2455 |          1        0.03       61.59
       2456 |          1        0.03       61.62
       2457 |          1        0.03       61.65
       2459 |          1        0.03       61.68
       2460 |          1        0.03       61.71
       2461 |          1        0.03       61.74
       2464 |          1        0.03       61.78
       2466 |          1        0.03       61.81
       2467 |          1        0.03       61.84
       2468 |          1        0.03       61.87
       2469 |          1        0.03       61.90
       2470 |          1        0.03       61.93
       2471 |          1        0.03       61.96
       2472 |          1        0.03       61.99
       2473 |          1        0.03       62.02
       2474 |          1        0.03       62.05
       2475 |          1        0.03       62.08
       2477 |          1        0.03       62.11
       2479 |          1        0.03       62.14
       2480 |          1        0.03       62.17
       2481 |          1        0.03       62.20
       2482 |          1        0.03       62.23
       2483 |          1        0.03       62.26
       2484 |          1        0.03       62.29
       2485 |          1        0.03       62.32
       2486 |          1        0.03       62.36
       2487 |          1        0.03       62.39
       2488 |          1        0.03       62.42
       2489 |          1        0.03       62.45
       2490 |          1        0.03       62.48
       2492 |          1        0.03       62.51
       2493 |          1        0.03       62.54
       2494 |          1        0.03       62.57
       2495 |          1        0.03       62.60
       2497 |          1        0.03       62.63
       2500 |          1        0.03       62.66
       2501 |          1        0.03       62.69
       2503 |          1        0.03       62.72
       2505 |          1        0.03       62.75
       2506 |          1        0.03       62.78
       2507 |          1        0.03       62.81
       2510 |          1        0.03       62.84
       2511 |          1        0.03       62.87
       2512 |          1        0.03       62.90
       2516 |          1        0.03       62.93
       2518 |          1        0.03       62.97
       2519 |          1        0.03       63.00
       2520 |          1        0.03       63.03
       2521 |          1        0.03       63.06
       2522 |          1        0.03       63.09
       2523 |          1        0.03       63.12
       2524 |          1        0.03       63.15
       2526 |          1        0.03       63.18
       2527 |          1        0.03       63.21
       2528 |          1        0.03       63.24
       2530 |          1        0.03       63.27
       2531 |          1        0.03       63.30
       2532 |          1        0.03       63.33
       2533 |          1        0.03       63.36
       2534 |          1        0.03       63.39
       2535 |          1        0.03       63.42
       2537 |          1        0.03       63.45
       2538 |          1        0.03       63.48
       2539 |          1        0.03       63.51
       2542 |          1        0.03       63.54
       2543 |          1        0.03       63.58
       2545 |          1        0.03       63.61
       2547 |          1        0.03       63.64
       2548 |          1        0.03       63.67
       2549 |          1        0.03       63.70
       2550 |          1        0.03       63.73
       2551 |          1        0.03       63.76
       2552 |          1        0.03       63.79
       2554 |          1        0.03       63.82
       2555 |          1        0.03       63.85
       2557 |          1        0.03       63.88
       2558 |          1        0.03       63.91
       2562 |          1        0.03       63.94
       2563 |          1        0.03       63.97
       2566 |          1        0.03       64.00
       2568 |          1        0.03       64.03
       2569 |          1        0.03       64.06
       2570 |          1        0.03       64.09
       2571 |          1        0.03       64.12
       2572 |          1        0.03       64.15
       2573 |          1        0.03       64.19
       2575 |          1        0.03       64.22
       2579 |          1        0.03       64.25
       2580 |          1        0.03       64.28
       2581 |          1        0.03       64.31
       2583 |          1        0.03       64.34
       2584 |          1        0.03       64.37
       2586 |          1        0.03       64.40
       2587 |          1        0.03       64.43
       2588 |          1        0.03       64.46
       2590 |          1        0.03       64.49
       2592 |          1        0.03       64.52
       2593 |          1        0.03       64.55
       2594 |          1        0.03       64.58
       2595 |          1        0.03       64.61
       2596 |          1        0.03       64.64
       2598 |          1        0.03       64.67
       2599 |          1        0.03       64.70
       2600 |          1        0.03       64.73
       2601 |          1        0.03       64.77
       2603 |          1        0.03       64.80
       2604 |          1        0.03       64.83
       2608 |          1        0.03       64.86
       2609 |          1        0.03       64.89
       2610 |          1        0.03       64.92
       2611 |          1        0.03       64.95
       2613 |          1        0.03       64.98
       2614 |          1        0.03       65.01
       2615 |          1        0.03       65.04
       2616 |          1        0.03       65.07
       2617 |          1        0.03       65.10
       2618 |          1        0.03       65.13
       2619 |          1        0.03       65.16
       2620 |          1        0.03       65.19
       2621 |          1        0.03       65.22
       2622 |          1        0.03       65.25
       2623 |          1        0.03       65.28
       2624 |          1        0.03       65.31
       2625 |          1        0.03       65.34
       2626 |          1        0.03       65.38
       2629 |          1        0.03       65.41
       2630 |          1        0.03       65.44
       2631 |          1        0.03       65.47
       2632 |          1        0.03       65.50
       2633 |          1        0.03       65.53
       2634 |          1        0.03       65.56
       2635 |          1        0.03       65.59
       2636 |          1        0.03       65.62
       2637 |          1        0.03       65.65
       2638 |          1        0.03       65.68
       2639 |          1        0.03       65.71
       2642 |          1        0.03       65.74
       2643 |          1        0.03       65.77
       2644 |          1        0.03       65.80
       2647 |          1        0.03       65.83
       2648 |          1        0.03       65.86
       2649 |          1        0.03       65.89
       2651 |          1        0.03       65.92
       2652 |          1        0.03       65.95
       2653 |          1        0.03       65.99
       2654 |          1        0.03       66.02
       2655 |          1        0.03       66.05
       2656 |          1        0.03       66.08
       2657 |          1        0.03       66.11
       2658 |          1        0.03       66.14
       2659 |          1        0.03       66.17
       2660 |          1        0.03       66.20
       2661 |          1        0.03       66.23
       2662 |          1        0.03       66.26
       2663 |          1        0.03       66.29
       2664 |          1        0.03       66.32
       2665 |          1        0.03       66.35
       2666 |          1        0.03       66.38
       2667 |          1        0.03       66.41
       2668 |          1        0.03       66.44
       2669 |          1        0.03       66.47
       2670 |          1        0.03       66.50
       2671 |          1        0.03       66.53
       2672 |          1        0.03       66.56
       2673 |          1        0.03       66.60
       2674 |          1        0.03       66.63
       2675 |          1        0.03       66.66
       2676 |          1        0.03       66.69
       2677 |          1        0.03       66.72
       2678 |          1        0.03       66.75
       2680 |          1        0.03       66.78
       2681 |          1        0.03       66.81
       2682 |          1        0.03       66.84
       2683 |          1        0.03       66.87
       2684 |          1        0.03       66.90
       2685 |          1        0.03       66.93
       2686 |          1        0.03       66.96
       2687 |          1        0.03       66.99
       2688 |          1        0.03       67.02
       2689 |          1        0.03       67.05
       2690 |          1        0.03       67.08
       2691 |          1        0.03       67.11
       2692 |          1        0.03       67.14
       2693 |          1        0.03       67.18
       2695 |          1        0.03       67.21
       2696 |          1        0.03       67.24
       2697 |          1        0.03       67.27
       2698 |          1        0.03       67.30
       2699 |          1        0.03       67.33
       2700 |          1        0.03       67.36
       2701 |          1        0.03       67.39
       2702 |          1        0.03       67.42
       2704 |          1        0.03       67.45
       2705 |          1        0.03       67.48
       2706 |          1        0.03       67.51
       2707 |          1        0.03       67.54
       2708 |          1        0.03       67.57
       2709 |          1        0.03       67.60
       2710 |          1        0.03       67.63
       2712 |          1        0.03       67.66
       2713 |          1        0.03       67.69
       2714 |          1        0.03       67.72
       2715 |          1        0.03       67.75
       2717 |          1        0.03       67.79
       2718 |          1        0.03       67.82
       2719 |          1        0.03       67.85
       2720 |          1        0.03       67.88
       2721 |          1        0.03       67.91
       2723 |          1        0.03       67.94
       2724 |          1        0.03       67.97
       2725 |          1        0.03       68.00
       2726 |          1        0.03       68.03
       2727 |          1        0.03       68.06
       2728 |          1        0.03       68.09
       2729 |          1        0.03       68.12
       2730 |          1        0.03       68.15
       2731 |          1        0.03       68.18
       2732 |          1        0.03       68.21
       2733 |          1        0.03       68.24
       2734 |          1        0.03       68.27
       2735 |          1        0.03       68.30
       2737 |          1        0.03       68.33
       2738 |          1        0.03       68.36
       2739 |          1        0.03       68.40
       2741 |          1        0.03       68.43
       2742 |          1        0.03       68.46
       2743 |          1        0.03       68.49
       2744 |          1        0.03       68.52
       2745 |          1        0.03       68.55
       2746 |          1        0.03       68.58
       2747 |          1        0.03       68.61
       2748 |          1        0.03       68.64
       2749 |          1        0.03       68.67
       2750 |          1        0.03       68.70
       2751 |          1        0.03       68.73
       2752 |          1        0.03       68.76
       2753 |          1        0.03       68.79
       2754 |          1        0.03       68.82
       2755 |          1        0.03       68.85
       2756 |          1        0.03       68.88
       2757 |          1        0.03       68.91
       2758 |          1        0.03       68.94
       2759 |          1        0.03       68.97
       2760 |          1        0.03       69.01
       2762 |          1        0.03       69.04
       2764 |          1        0.03       69.07
       2765 |          1        0.03       69.10
       2766 |          1        0.03       69.13
       2767 |          1        0.03       69.16
       2768 |          1        0.03       69.19
       2770 |          1        0.03       69.22
       2771 |          1        0.03       69.25
       2772 |          1        0.03       69.28
       2773 |          1        0.03       69.31
       2774 |          1        0.03       69.34
       2775 |          1        0.03       69.37
       2777 |          1        0.03       69.40
       2778 |          1        0.03       69.43
       2779 |          1        0.03       69.46
       2780 |          1        0.03       69.49
       2781 |          1        0.03       69.52
       2782 |          1        0.03       69.55
       2783 |          1        0.03       69.59
       2784 |          1        0.03       69.62
       2785 |          1        0.03       69.65
       2786 |          1        0.03       69.68
       2787 |          1        0.03       69.71
       2788 |          1        0.03       69.74
       2789 |          1        0.03       69.77
       2790 |          1        0.03       69.80
       2792 |          1        0.03       69.83
       2793 |          1        0.03       69.86
       2794 |          1        0.03       69.89
       2795 |          1        0.03       69.92
       2796 |          1        0.03       69.95
       2797 |          1        0.03       69.98
       2798 |          1        0.03       70.01
       2799 |          1        0.03       70.04
       2801 |          1        0.03       70.07
       2802 |          1        0.03       70.10
       2803 |          1        0.03       70.13
       2804 |          1        0.03       70.16
       2805 |          1        0.03       70.20
       2806 |          1        0.03       70.23
       2808 |          1        0.03       70.26
       2809 |          1        0.03       70.29
       2810 |          1        0.03       70.32
       2811 |          1        0.03       70.35
       2812 |          1        0.03       70.38
       2813 |          1        0.03       70.41
       2814 |          1        0.03       70.44
       2816 |          1        0.03       70.47
       2817 |          1        0.03       70.50
       2818 |          1        0.03       70.53
       2820 |          1        0.03       70.56
       2821 |          1        0.03       70.59
       2822 |          1        0.03       70.62
       2824 |          1        0.03       70.65
       2826 |          1        0.03       70.68
       2827 |          1        0.03       70.71
       2828 |          1        0.03       70.74
       2830 |          1        0.03       70.77
       2831 |          1        0.03       70.81
       2832 |          1        0.03       70.84
       2833 |          1        0.03       70.87
       2834 |          1        0.03       70.90
       2835 |          1        0.03       70.93
       2836 |          1        0.03       70.96
       2838 |          1        0.03       70.99
       2840 |          1        0.03       71.02
       2842 |          1        0.03       71.05
       2844 |          1        0.03       71.08
       2845 |          1        0.03       71.11
       2846 |          1        0.03       71.14
       2847 |          1        0.03       71.17
       2848 |          1        0.03       71.20
       2849 |          1        0.03       71.23
       2851 |          1        0.03       71.26
       2852 |          1        0.03       71.29
       2853 |          1        0.03       71.32
       2855 |          1        0.03       71.35
       2856 |          1        0.03       71.38
       2857 |          1        0.03       71.42
       2858 |          1        0.03       71.45
       2859 |          1        0.03       71.48
       2860 |          1        0.03       71.51
       2861 |          1        0.03       71.54
       2862 |          1        0.03       71.57
       2863 |          1        0.03       71.60
       2864 |          1        0.03       71.63
       2866 |          1        0.03       71.66
       2868 |          1        0.03       71.69
       2869 |          1        0.03       71.72
       2871 |          1        0.03       71.75
       2872 |          1        0.03       71.78
       2873 |          1        0.03       71.81
       2874 |          1        0.03       71.84
       2875 |          1        0.03       71.87
       2876 |          1        0.03       71.90
       2877 |          1        0.03       71.93
       2878 |          1        0.03       71.96
       2879 |          1        0.03       72.00
       2880 |          1        0.03       72.03
       2882 |          1        0.03       72.06
       2883 |          1        0.03       72.09
       2885 |          1        0.03       72.12
       2887 |          1        0.03       72.15
       2888 |          1        0.03       72.18
       2889 |          1        0.03       72.21
       2891 |          1        0.03       72.24
       2893 |          1        0.03       72.27
       2894 |          1        0.03       72.30
       2896 |          1        0.03       72.33
       2897 |          1        0.03       72.36
       2898 |          1        0.03       72.39
       2899 |          1        0.03       72.42
       2900 |          1        0.03       72.45
       2901 |          1        0.03       72.48
       2902 |          1        0.03       72.51
       2903 |          1        0.03       72.54
       2904 |          1        0.03       72.57
       2905 |          1        0.03       72.61
       2906 |          1        0.03       72.64
       2907 |          1        0.03       72.67
       2908 |          1        0.03       72.70
       2909 |          1        0.03       72.73
       2910 |          1        0.03       72.76
       2912 |          1        0.03       72.79
       2913 |          1        0.03       72.82
       2914 |          1        0.03       72.85
       2915 |          1        0.03       72.88
       2916 |          1        0.03       72.91
       2918 |          1        0.03       72.94
       2919 |          1        0.03       72.97
       2920 |          1        0.03       73.00
       2921 |          1        0.03       73.03
       2922 |          1        0.03       73.06
       2923 |          1        0.03       73.09
       2924 |          1        0.03       73.12
       2925 |          1        0.03       73.15
       2926 |          1        0.03       73.18
       2927 |          1        0.03       73.22
       2928 |          1        0.03       73.25
       2929 |          1        0.03       73.28
       2930 |          1        0.03       73.31
       2931 |          1        0.03       73.34
       2932 |          1        0.03       73.37
       2933 |          1        0.03       73.40
       2934 |          1        0.03       73.43
       2937 |          1        0.03       73.46
       2938 |          1        0.03       73.49
       2940 |          1        0.03       73.52
       2942 |          1        0.03       73.55
       2943 |          1        0.03       73.58
       2945 |          1        0.03       73.61
       2946 |          1        0.03       73.64
       2947 |          1        0.03       73.67
       2948 |          1        0.03       73.70
       2949 |          1        0.03       73.73
       2952 |          1        0.03       73.76
       2954 |          1        0.03       73.79
       2955 |          1        0.03       73.83
       2956 |          1        0.03       73.86
       2957 |          1        0.03       73.89
       2958 |          1        0.03       73.92
       2959 |          1        0.03       73.95
       2960 |          1        0.03       73.98
       2962 |          1        0.03       74.01
       2963 |          1        0.03       74.04
       2964 |          1        0.03       74.07
       2965 |          1        0.03       74.10
       2966 |          1        0.03       74.13
       2968 |          1        0.03       74.16
       2969 |          1        0.03       74.19
       2970 |          1        0.03       74.22
       2971 |          1        0.03       74.25
       2972 |          1        0.03       74.28
       2973 |          1        0.03       74.31
       2974 |          1        0.03       74.34
       2975 |          1        0.03       74.37
       2976 |          1        0.03       74.41
       2978 |          1        0.03       74.44
       2979 |          1        0.03       74.47
       2980 |          1        0.03       74.50
       2981 |          1        0.03       74.53
       2984 |          1        0.03       74.56
       2985 |          1        0.03       74.59
       2987 |          1        0.03       74.62
       2988 |          1        0.03       74.65
       2989 |          1        0.03       74.68
       2990 |          1        0.03       74.71
       2991 |          1        0.03       74.74
       2992 |          1        0.03       74.77
       2994 |          1        0.03       74.80
       2995 |          1        0.03       74.83
       2996 |          1        0.03       74.86
       2997 |          1        0.03       74.89
       2998 |          1        0.03       74.92
       2999 |          1        0.03       74.95
       3000 |          1        0.03       74.98
       3001 |          1        0.03       75.02
       3002 |          1        0.03       75.05
       3003 |          1        0.03       75.08
       3004 |          1        0.03       75.11
       3005 |          1        0.03       75.14
       3006 |          1        0.03       75.17
       3007 |          1        0.03       75.20
       3008 |          1        0.03       75.23
       3009 |          1        0.03       75.26
       3010 |          1        0.03       75.29
       3011 |          1        0.03       75.32
       3012 |          1        0.03       75.35
       3013 |          1        0.03       75.38
       3014 |          1        0.03       75.41
       3015 |          1        0.03       75.44
       3016 |          1        0.03       75.47
       3018 |          1        0.03       75.50
       3019 |          1        0.03       75.53
       3020 |          1        0.03       75.56
       3021 |          1        0.03       75.59
       3022 |          1        0.03       75.63
       3023 |          1        0.03       75.66
       3024 |          1        0.03       75.69
       3025 |          1        0.03       75.72
       3026 |          1        0.03       75.75
       3027 |          1        0.03       75.78
       3028 |          1        0.03       75.81
       3029 |          1        0.03       75.84
       3030 |          1        0.03       75.87
       3031 |          1        0.03       75.90
       3032 |          1        0.03       75.93
       3033 |          1        0.03       75.96
       3034 |          1        0.03       75.99
       3035 |          1        0.03       76.02
       3036 |          1        0.03       76.05
       3037 |          1        0.03       76.08
       3038 |          1        0.03       76.11
       3039 |          1        0.03       76.14
       3040 |          1        0.03       76.17
       3041 |          1        0.03       76.21
       3042 |          1        0.03       76.24
       3044 |          1        0.03       76.27
       3046 |          1        0.03       76.30
       3047 |          1        0.03       76.33
       3048 |          1        0.03       76.36
       3049 |          1        0.03       76.39
       3050 |          1        0.03       76.42
       3051 |          1        0.03       76.45
       3052 |          1        0.03       76.48
       3053 |          1        0.03       76.51
       3054 |          1        0.03       76.54
       3055 |          1        0.03       76.57
       3056 |          1        0.03       76.60
       3057 |          1        0.03       76.63
       3058 |          1        0.03       76.66
       3059 |          1        0.03       76.69
       3060 |          1        0.03       76.72
       3061 |          1        0.03       76.75
       3062 |          1        0.03       76.78
       3063 |          1        0.03       76.82
       3064 |          1        0.03       76.85
       3065 |          1        0.03       76.88
       3066 |          1        0.03       76.91
       3068 |          1        0.03       76.94
       3069 |          1        0.03       76.97
       3070 |          1        0.03       77.00
       3072 |          1        0.03       77.03
       3075 |          1        0.03       77.06
       3076 |          1        0.03       77.09
       3078 |          1        0.03       77.12
       3079 |          1        0.03       77.15
       3080 |          1        0.03       77.18
       3081 |          1        0.03       77.21
       3083 |          1        0.03       77.24
       3084 |          1        0.03       77.27
       3085 |          1        0.03       77.30
       3086 |          1        0.03       77.33
       3087 |          1        0.03       77.36
       3089 |          1        0.03       77.39
       3090 |          1        0.03       77.43
       3091 |          1        0.03       77.46
       3092 |          1        0.03       77.49
       3093 |          1        0.03       77.52
       3094 |          1        0.03       77.55
       3096 |          1        0.03       77.58
       3097 |          1        0.03       77.61
       3098 |          1        0.03       77.64
       3099 |          1        0.03       77.67
       3100 |          1        0.03       77.70
       3101 |          1        0.03       77.73
       3102 |          1        0.03       77.76
       3108 |          1        0.03       77.79
       3109 |          1        0.03       77.82
       3110 |          1        0.03       77.85
       3111 |          1        0.03       77.88
       3113 |          1        0.03       77.91
       3114 |          1        0.03       77.94
       3115 |          1        0.03       77.97
       3116 |          1        0.03       78.00
       3117 |          1        0.03       78.04
       3120 |          1        0.03       78.07
       3121 |          1        0.03       78.10
       3122 |          1        0.03       78.13
       3123 |          1        0.03       78.16
       3124 |          1        0.03       78.19
       3125 |          1        0.03       78.22
       3126 |          1        0.03       78.25
       3127 |          1        0.03       78.28
       3128 |          1        0.03       78.31
       3129 |          1        0.03       78.34
       3130 |          1        0.03       78.37
       3131 |          1        0.03       78.40
       3132 |          1        0.03       78.43
       3133 |          1        0.03       78.46
       3135 |          1        0.03       78.49
       3137 |          1        0.03       78.52
       3138 |          1        0.03       78.55
       3139 |          1        0.03       78.58
       3141 |          1        0.03       78.62
       3142 |          1        0.03       78.65
       3143 |          1        0.03       78.68
       3144 |          1        0.03       78.71
       3145 |          1        0.03       78.74
       3146 |          1        0.03       78.77
       3147 |          1        0.03       78.80
       3148 |          1        0.03       78.83
       3149 |          1        0.03       78.86
       3153 |          1        0.03       78.89
       3155 |          1        0.03       78.92
       3156 |          1        0.03       78.95
       3158 |          1        0.03       78.98
       3159 |          1        0.03       79.01
       3161 |          1        0.03       79.04
       3162 |          1        0.03       79.07
       3163 |          1        0.03       79.10
       3164 |          1        0.03       79.13
       3165 |          1        0.03       79.16
       3166 |          1        0.03       79.19
       3167 |          1        0.03       79.23
       3170 |          1        0.03       79.26
       3171 |          1        0.03       79.29
       3174 |          1        0.03       79.32
       3176 |          1        0.03       79.35
       3177 |          1        0.03       79.38
       3179 |          1        0.03       79.41
       3180 |          1        0.03       79.44
       3181 |          1        0.03       79.47
       3182 |          1        0.03       79.50
       3184 |          1        0.03       79.53
       3185 |          1        0.03       79.56
       3186 |          1        0.03       79.59
       3187 |          1        0.03       79.62
       3189 |          1        0.03       79.65
       3191 |          1        0.03       79.68
       3193 |          1        0.03       79.71
       3194 |          1        0.03       79.74
       3195 |          1        0.03       79.77
       3196 |          1        0.03       79.80
       3202 |          1        0.03       79.84
       3206 |          1        0.03       79.87
       3207 |          1        0.03       79.90
       3208 |          1        0.03       79.93
       3212 |          1        0.03       79.96
       3213 |          1        0.03       79.99
       3214 |          1        0.03       80.02
       3215 |          1        0.03       80.05
       3217 |          1        0.03       80.08
       3219 |          1        0.03       80.11
       3220 |          1        0.03       80.14
       3221 |          1        0.03       80.17
       3222 |          1        0.03       80.20
       3224 |          1        0.03       80.23
       3225 |          1        0.03       80.26
       3226 |          1        0.03       80.29
       3229 |          1        0.03       80.32
       3231 |          1        0.03       80.35
       3232 |          1        0.03       80.38
       3234 |          1        0.03       80.41
       3235 |          1        0.03       80.45
       3236 |          1        0.03       80.48
       3237 |          1        0.03       80.51
       3238 |          1        0.03       80.54
       3239 |          1        0.03       80.57
       3240 |          1        0.03       80.60
       3241 |          1        0.03       80.63
       3242 |          1        0.03       80.66
       3244 |          1        0.03       80.69
       3245 |          1        0.03       80.72
       3246 |          1        0.03       80.75
       3248 |          1        0.03       80.78
       3250 |          1        0.03       80.81
       3251 |          1        0.03       80.84
       3252 |          1        0.03       80.87
       3255 |          1        0.03       80.90
       3256 |          1        0.03       80.93
       3257 |          1        0.03       80.96
       3258 |          1        0.03       80.99
       3260 |          1        0.03       81.03
       3261 |          1        0.03       81.06
       3262 |          1        0.03       81.09
       3263 |          1        0.03       81.12
       3265 |          1        0.03       81.15
       3266 |          1        0.03       81.18
       3267 |          1        0.03       81.21
       3269 |          1        0.03       81.24
       3271 |          1        0.03       81.27
       3272 |          1        0.03       81.30
       3273 |          1        0.03       81.33
       3274 |          1        0.03       81.36
       3275 |          1        0.03       81.39
       3276 |          1        0.03       81.42
       3277 |          1        0.03       81.45
       3278 |          1        0.03       81.48
       3282 |          1        0.03       81.51
       3284 |          1        0.03       81.54
       3285 |          1        0.03       81.57
       3287 |          1        0.03       81.60
       3288 |          1        0.03       81.64
       3289 |          1        0.03       81.67
       3293 |          1        0.03       81.70
       3294 |          1        0.03       81.73
       3296 |          1        0.03       81.76
       3303 |          1        0.03       81.79
       3305 |          1        0.03       81.82
       3307 |          1        0.03       81.85
       3309 |          1        0.03       81.88
       3313 |          1        0.03       81.91
       3314 |          1        0.03       81.94
       3316 |          1        0.03       81.97
       3317 |          1        0.03       82.00
       3318 |          1        0.03       82.03
       3319 |          1        0.03       82.06
       3321 |          1        0.03       82.09
       3322 |          1        0.03       82.12
       3323 |          1        0.03       82.15
       3325 |          1        0.03       82.18
       3326 |          1        0.03       82.21
       3327 |          1        0.03       82.25
       3328 |          1        0.03       82.28
       3331 |          1        0.03       82.31
       3332 |          1        0.03       82.34
       3333 |          1        0.03       82.37
       3335 |          1        0.03       82.40
       3336 |          1        0.03       82.43
       3338 |          1        0.03       82.46
       3339 |          1        0.03       82.49
       3340 |          1        0.03       82.52
       3341 |          1        0.03       82.55
       3342 |          1        0.03       82.58
       3343 |          1        0.03       82.61
       3344 |          1        0.03       82.64
       3346 |          1        0.03       82.67
       3347 |          1        0.03       82.70
       3348 |          1        0.03       82.73
       3351 |          1        0.03       82.76
       3352 |          1        0.03       82.79
       3358 |          1        0.03       82.82
       3361 |          1        0.03       82.86
       3362 |          1        0.03       82.89
       3363 |          1        0.03       82.92
       3364 |          1        0.03       82.95
       3365 |          1        0.03       82.98
       3367 |          1        0.03       83.01
       3368 |          1        0.03       83.04
       3369 |          1        0.03       83.07
       3372 |          1        0.03       83.10
       3375 |          1        0.03       83.13
       3377 |          1        0.03       83.16
       3381 |          1        0.03       83.19
       3382 |          1        0.03       83.22
       3383 |          1        0.03       83.25
       3384 |          1        0.03       83.28
       3385 |          1        0.03       83.31
       3386 |          1        0.03       83.34
       3387 |          1        0.03       83.37
       3388 |          1        0.03       83.40
       3390 |          1        0.03       83.44
       3391 |          1        0.03       83.47
       3392 |          1        0.03       83.50
       3393 |          1        0.03       83.53
       3394 |          1        0.03       83.56
       3396 |          1        0.03       83.59
       3398 |          1        0.03       83.62
       3400 |          1        0.03       83.65
       3401 |          1        0.03       83.68
       3402 |          1        0.03       83.71
       3403 |          1        0.03       83.74
       3405 |          1        0.03       83.77
       3406 |          1        0.03       83.80
       3408 |          1        0.03       83.83
       3414 |          1        0.03       83.86
       3418 |          1        0.03       83.89
       3420 |          1        0.03       83.92
       3421 |          1        0.03       83.95
       3422 |          1        0.03       83.98
       3424 |          1        0.03       84.01
       3425 |          1        0.03       84.05
       3426 |          1        0.03       84.08
       3428 |          1        0.03       84.11
       3429 |          1        0.03       84.14
       3430 |          1        0.03       84.17
       3432 |          1        0.03       84.20
       3433 |          1        0.03       84.23
       3435 |          1        0.03       84.26
       3436 |          1        0.03       84.29
       3437 |          1        0.03       84.32
       3438 |          1        0.03       84.35
       3439 |          1        0.03       84.38
       3440 |          1        0.03       84.41
       3442 |          1        0.03       84.44
       3443 |          1        0.03       84.47
       3444 |          1        0.03       84.50
       3445 |          1        0.03       84.53
       3446 |          1        0.03       84.56
       3447 |          1        0.03       84.59
       3448 |          1        0.03       84.62
       3449 |          1        0.03       84.66
       3450 |          1        0.03       84.69
       3451 |          1        0.03       84.72
       3452 |          1        0.03       84.75
       3453 |          1        0.03       84.78
       3454 |          1        0.03       84.81
       3455 |          1        0.03       84.84
       3456 |          1        0.03       84.87
       3457 |          1        0.03       84.90
       3459 |          1        0.03       84.93
       3460 |          1        0.03       84.96
       3461 |          1        0.03       84.99
       3462 |          1        0.03       85.02
       3463 |          1        0.03       85.05
       3464 |          1        0.03       85.08
       3466 |          1        0.03       85.11
       3468 |          1        0.03       85.14
       3469 |          1        0.03       85.17
       3470 |          1        0.03       85.20
       3471 |          1        0.03       85.23
       3472 |          1        0.03       85.27
       3473 |          1        0.03       85.30
       3474 |          1        0.03       85.33
       3477 |          1        0.03       85.36
       3478 |          1        0.03       85.39
       3479 |          1        0.03       85.42
       3480 |          1        0.03       85.45
       3481 |          1        0.03       85.48
       3483 |          1        0.03       85.51
       3484 |          1        0.03       85.54
       3485 |          1        0.03       85.57
       3487 |          1        0.03       85.60
       3488 |          1        0.03       85.63
       3489 |          1        0.03       85.66
       3490 |          1        0.03       85.69
       3491 |          1        0.03       85.72
       3493 |          1        0.03       85.75
       3494 |          1        0.03       85.78
       3495 |          1        0.03       85.81
       3496 |          1        0.03       85.85
       3498 |          1        0.03       85.88
       3499 |          1        0.03       85.91
       3500 |          1        0.03       85.94
       3501 |          1        0.03       85.97
       3502 |          1        0.03       86.00
       3503 |          1        0.03       86.03
       3504 |          1        0.03       86.06
       3505 |          1        0.03       86.09
       3506 |          1        0.03       86.12
       3508 |          1        0.03       86.15
       3509 |          1        0.03       86.18
       3511 |          1        0.03       86.21
       3512 |          1        0.03       86.24
       3513 |          1        0.03       86.27
       3514 |          1        0.03       86.30
       3515 |          1        0.03       86.33
       3516 |          1        0.03       86.36
       3518 |          1        0.03       86.39
       3519 |          1        0.03       86.42
       3521 |          1        0.03       86.46
       3522 |          1        0.03       86.49
       3525 |          1        0.03       86.52
       3526 |          1        0.03       86.55
       3527 |          1        0.03       86.58
       3528 |          1        0.03       86.61
       3529 |          1        0.03       86.64
       3530 |          1        0.03       86.67
       3531 |          1        0.03       86.70
       3532 |          1        0.03       86.73
       3533 |          1        0.03       86.76
       3534 |          1        0.03       86.79
       3539 |          1        0.03       86.82
       3540 |          1        0.03       86.85
       3541 |          1        0.03       86.88
       3542 |          1        0.03       86.91
       3543 |          1        0.03       86.94
       3544 |          1        0.03       86.97
       3546 |          1        0.03       87.00
       3548 |          1        0.03       87.03
       3549 |          1        0.03       87.07
       3550 |          1        0.03       87.10
       3551 |          1        0.03       87.13
       3553 |          1        0.03       87.16
       3554 |          1        0.03       87.19
       3555 |          1        0.03       87.22
       3556 |          1        0.03       87.25
       3557 |          1        0.03       87.28
       3558 |          1        0.03       87.31
       3560 |          1        0.03       87.34
       3561 |          1        0.03       87.37
       3562 |          1        0.03       87.40
       3563 |          1        0.03       87.43
       3565 |          1        0.03       87.46
       3566 |          1        0.03       87.49
       3567 |          1        0.03       87.52
       3568 |          1        0.03       87.55
       3571 |          1        0.03       87.58
       3573 |          1        0.03       87.61
       3574 |          1        0.03       87.64
       3577 |          1        0.03       87.68
       3578 |          1        0.03       87.71
       3581 |          1        0.03       87.74
       3583 |          1        0.03       87.77
       3585 |          1        0.03       87.80
       3586 |          1        0.03       87.83
       3587 |          1        0.03       87.86
       3588 |          1        0.03       87.89
       3589 |          1        0.03       87.92
       3590 |          1        0.03       87.95
       3591 |          1        0.03       87.98
       3592 |          1        0.03       88.01
       3593 |          1        0.03       88.04
       3594 |          1        0.03       88.07
       3595 |          1        0.03       88.10
       3596 |          1        0.03       88.13
       3597 |          1        0.03       88.16
       3598 |          1        0.03       88.19
       3599 |          1        0.03       88.22
       3602 |          1        0.03       88.26
       3603 |          1        0.03       88.29
       3604 |          1        0.03       88.32
       3605 |          1        0.03       88.35
       3606 |          1        0.03       88.38
       3607 |          1        0.03       88.41
       3608 |          1        0.03       88.44
       3609 |          1        0.03       88.47
       3610 |          1        0.03       88.50
       3611 |          1        0.03       88.53
       3613 |          1        0.03       88.56
       3614 |          1        0.03       88.59
       3615 |          1        0.03       88.62
       3617 |          1        0.03       88.65
       3618 |          1        0.03       88.68
       3619 |          1        0.03       88.71
       3620 |          1        0.03       88.74
       3624 |          1        0.03       88.77
       3626 |          1        0.03       88.80
       3627 |          1        0.03       88.83
       3628 |          1        0.03       88.87
       3629 |          1        0.03       88.90
       3630 |          1        0.03       88.93
       3631 |          1        0.03       88.96
       3632 |          1        0.03       88.99
       3633 |          1        0.03       89.02
       3635 |          1        0.03       89.05
       3636 |          1        0.03       89.08
       3637 |          1        0.03       89.11
       3638 |          1        0.03       89.14
       3639 |          1        0.03       89.17
       3640 |          1        0.03       89.20
       3642 |          1        0.03       89.23
       3643 |          1        0.03       89.26
       3645 |          1        0.03       89.29
       3646 |          1        0.03       89.32
       3649 |          1        0.03       89.35
       3650 |          1        0.03       89.38
       3651 |          1        0.03       89.41
       3652 |          1        0.03       89.44
       3653 |          1        0.03       89.48
       3654 |          1        0.03       89.51
       3655 |          1        0.03       89.54
       3656 |          1        0.03       89.57
       3658 |          1        0.03       89.60
       3659 |          1        0.03       89.63
       3660 |          1        0.03       89.66
       3661 |          1        0.03       89.69
       3662 |          1        0.03       89.72
       3664 |          1        0.03       89.75
       3665 |          1        0.03       89.78
       3666 |          1        0.03       89.81
       3668 |          1        0.03       89.84
       3669 |          1        0.03       89.87
       3670 |          1        0.03       89.90
       3671 |          1        0.03       89.93
       3672 |          1        0.03       89.96
       3674 |          1        0.03       89.99
       3675 |          1        0.03       90.02
       3676 |          1        0.03       90.05
       3677 |          1        0.03       90.09
       3678 |          1        0.03       90.12
       3679 |          1        0.03       90.15
       3680 |          1        0.03       90.18
       3681 |          1        0.03       90.21
       3683 |          1        0.03       90.24
       3691 |          1        0.03       90.27
       3692 |          1        0.03       90.30
       3693 |          1        0.03       90.33
       3694 |          1        0.03       90.36
       3695 |          1        0.03       90.39
       3697 |          1        0.03       90.42
       3701 |          1        0.03       90.45
       3703 |          1        0.03       90.48
       3704 |          1        0.03       90.51
       3705 |          1        0.03       90.54
       3706 |          1        0.03       90.57
       3709 |          1        0.03       90.60
       3710 |          1        0.03       90.63
       3711 |          1        0.03       90.67
       3712 |          1        0.03       90.70
       3713 |          1        0.03       90.73
       3714 |          1        0.03       90.76
       3715 |          1        0.03       90.79
       3717 |          1        0.03       90.82
       3718 |          1        0.03       90.85
       3720 |          1        0.03       90.88
       3721 |          1        0.03       90.91
       3722 |          1        0.03       90.94
       3723 |          1        0.03       90.97
       3724 |          1        0.03       91.00
       3725 |          1        0.03       91.03
       3726 |          1        0.03       91.06
       3727 |          1        0.03       91.09
       3729 |          1        0.03       91.12
       3732 |          1        0.03       91.15
       3733 |          1        0.03       91.18
       3736 |          1        0.03       91.21
       3738 |          1        0.03       91.24
       3740 |          1        0.03       91.28
       3741 |          1        0.03       91.31
       3742 |          1        0.03       91.34
       3743 |          1        0.03       91.37
       3744 |          1        0.03       91.40
       3745 |          1        0.03       91.43
       3746 |          1        0.03       91.46
       3747 |          1        0.03       91.49
       3749 |          1        0.03       91.52
       3750 |          1        0.03       91.55
       3752 |          1        0.03       91.58
       3753 |          1        0.03       91.61
       3754 |          1        0.03       91.64
       3755 |          1        0.03       91.67
       3756 |          1        0.03       91.70
       3757 |          1        0.03       91.73
       3758 |          1        0.03       91.76
       3759 |          1        0.03       91.79
       3761 |          1        0.03       91.82
       3762 |          1        0.03       91.85
       3764 |          1        0.03       91.89
       3765 |          1        0.03       91.92
       3766 |          1        0.03       91.95
       3767 |          1        0.03       91.98
       3768 |          1        0.03       92.01
       3769 |          1        0.03       92.04
       3772 |          1        0.03       92.07
       3773 |          1        0.03       92.10
       3774 |          1        0.03       92.13
       3775 |          1        0.03       92.16
       3776 |          1        0.03       92.19
       3777 |          1        0.03       92.22
       3778 |          1        0.03       92.25
       3779 |          1        0.03       92.28
       3780 |          1        0.03       92.31
       3782 |          1        0.03       92.34
       3784 |          1        0.03       92.37
       3785 |          1        0.03       92.40
       3786 |          1        0.03       92.43
       3788 |          1        0.03       92.46
       3789 |          1        0.03       92.50
       3790 |          1        0.03       92.53
       3792 |          1        0.03       92.56
       3793 |          1        0.03       92.59
       3794 |          1        0.03       92.62
       3795 |          1        0.03       92.65
       3796 |          1        0.03       92.68
       3798 |          1        0.03       92.71
       3799 |          1        0.03       92.74
       3800 |          1        0.03       92.77
       3801 |          1        0.03       92.80
       3802 |          1        0.03       92.83
       3803 |          1        0.03       92.86
       3804 |          1        0.03       92.89
       3806 |          1        0.03       92.92
       3808 |          1        0.03       92.95
       3809 |          1        0.03       92.98
       3810 |          1        0.03       93.01
       3812 |          1        0.03       93.04
       3813 |          1        0.03       93.08
       3815 |          1        0.03       93.11
       3816 |          1        0.03       93.14
       3817 |          1        0.03       93.17
       3818 |          1        0.03       93.20
       3819 |          1        0.03       93.23
       3820 |          1        0.03       93.26
       3821 |          1        0.03       93.29
       3823 |          1        0.03       93.32
       3824 |          1        0.03       93.35
       3825 |          1        0.03       93.38
       3826 |          1        0.03       93.41
       3827 |          1        0.03       93.44
       3828 |          1        0.03       93.47
       3829 |          1        0.03       93.50
       3830 |          1        0.03       93.53
       3831 |          1        0.03       93.56
       3832 |          1        0.03       93.59
       3833 |          1        0.03       93.62
       3834 |          1        0.03       93.65
       3835 |          1        0.03       93.69
       3836 |          1        0.03       93.72
       3837 |          1        0.03       93.75
       3838 |          1        0.03       93.78
       3839 |          1        0.03       93.81
       3840 |          1        0.03       93.84
       3841 |          1        0.03       93.87
       3842 |          1        0.03       93.90
       3844 |          1        0.03       93.93
       3845 |          1        0.03       93.96
       3848 |          1        0.03       93.99
       3849 |          1        0.03       94.02
       3850 |          1        0.03       94.05
       3851 |          1        0.03       94.08
       3852 |          1        0.03       94.11
       3854 |          1        0.03       94.14
       3855 |          1        0.03       94.17
       3856 |          1        0.03       94.20
       3857 |          1        0.03       94.23
       3858 |          1        0.03       94.26
       3859 |          1        0.03       94.30
       3860 |          1        0.03       94.33
       3861 |          1        0.03       94.36
       3862 |          1        0.03       94.39
       3864 |          1        0.03       94.42
       3868 |          1        0.03       94.45
       3869 |          1        0.03       94.48
       3870 |          1        0.03       94.51
       3871 |          1        0.03       94.54
       3872 |          1        0.03       94.57
       3873 |          1        0.03       94.60
       3874 |          1        0.03       94.63
       3875 |          1        0.03       94.66
       3876 |          1        0.03       94.69
       3877 |          1        0.03       94.72
       3878 |          1        0.03       94.75
       3879 |          1        0.03       94.78
       3883 |          1        0.03       94.81
       3886 |          1        0.03       94.84
       3888 |          1        0.03       94.87
       3890 |          1        0.03       94.91
       3891 |          1        0.03       94.94
       3892 |          1        0.03       94.97
       3893 |          1        0.03       95.00
       3896 |          1        0.03       95.03
       3899 |          1        0.03       95.06
       3901 |          1        0.03       95.09
       3903 |          1        0.03       95.12
       3904 |          1        0.03       95.15
       3905 |          1        0.03       95.18
       3906 |          1        0.03       95.21
       3907 |          1        0.03       95.24
       3908 |          1        0.03       95.27
       3913 |          1        0.03       95.30
       3917 |          1        0.03       95.33
       3918 |          1        0.03       95.36
       3919 |          1        0.03       95.39
       3921 |          1        0.03       95.42
       3923 |          1        0.03       95.45
       3924 |          1        0.03       95.49
       3926 |          1        0.03       95.52
       3928 |          1        0.03       95.55
       3930 |          1        0.03       95.58
       3931 |          1        0.03       95.61
       3932 |          1        0.03       95.64
       3937 |          1        0.03       95.67
       3939 |          1        0.03       95.70
       3940 |          1        0.03       95.73
       3942 |          1        0.03       95.76
       3943 |          1        0.03       95.79
       3944 |          1        0.03       95.82
       3946 |          1        0.03       95.85
       3954 |          1        0.03       95.88
       3955 |          1        0.03       95.91
       3958 |          1        0.03       95.94
       3959 |          1        0.03       95.97
       3961 |          1        0.03       96.00
       3962 |          1        0.03       96.03
       3963 |          1        0.03       96.06
       3970 |          1        0.03       96.10
       3971 |          1        0.03       96.13
       3972 |          1        0.03       96.16
       3973 |          1        0.03       96.19
       3974 |          1        0.03       96.22
       3977 |          1        0.03       96.25
       3978 |          1        0.03       96.28
       3980 |          1        0.03       96.31
       3981 |          1        0.03       96.34
       3982 |          1        0.03       96.37
       3986 |          1        0.03       96.40
       3987 |          1        0.03       96.43
       3989 |          1        0.03       96.46
       3990 |          1        0.03       96.49
       3991 |          1        0.03       96.52
       3993 |          1        0.03       96.55
       3995 |          1        0.03       96.58
       3998 |          1        0.03       96.61
       3999 |          1        0.03       96.64
       4005 |          1        0.03       96.67
       4009 |          1        0.03       96.71
       4010 |          1        0.03       96.74
       4012 |          1        0.03       96.77
       4013 |          1        0.03       96.80
       4015 |          1        0.03       96.83
       4021 |          1        0.03       96.86
       4023 |          1        0.03       96.89
       4024 |          1        0.03       96.92
       4025 |          1        0.03       96.95
       4026 |          1        0.03       96.98
       4029 |          1        0.03       97.01
       4033 |          1        0.03       97.04
       4034 |          1        0.03       97.07
       4038 |          1        0.03       97.10
       4039 |          1        0.03       97.13
       4042 |          1        0.03       97.16
       4043 |          1        0.03       97.19
       4045 |          1        0.03       97.22
       4046 |          1        0.03       97.25
       4047 |          1        0.03       97.28
       4053 |          1        0.03       97.32
       4056 |          1        0.03       97.35
       4068 |          1        0.03       97.38
       4070 |          1        0.03       97.41
       4073 |          1        0.03       97.44
       4083 |          1        0.03       97.47
       4087 |          1        0.03       97.50
       4096 |          1        0.03       97.53
       4102 |          1        0.03       97.56
       4107 |          1        0.03       97.59
       4109 |          1        0.03       97.62
       4117 |          1        0.03       97.65
       4118 |          1        0.03       97.68
       4122 |          1        0.03       97.71
       4123 |          1        0.03       97.74
       4154 |          1        0.03       97.77
       4155 |          1        0.03       97.80
       4156 |          1        0.03       97.83
       4157 |          1        0.03       97.86
       4158 |          1        0.03       97.90
       4159 |          1        0.03       97.93
       4160 |          1        0.03       97.96
       4161 |          1        0.03       97.99
       4162 |          1        0.03       98.02
       4163 |          1        0.03       98.05
       4164 |          1        0.03       98.08
       4165 |          1        0.03       98.11
       4166 |          1        0.03       98.14
       4167 |          1        0.03       98.17
       4168 |          1        0.03       98.20
       4169 |          1        0.03       98.23
       4171 |          1        0.03       98.26
       4174 |          1        0.03       98.29
       4175 |          1        0.03       98.32
       4176 |          1        0.03       98.35
       4177 |          1        0.03       98.38
       4178 |          1        0.03       98.41
       4179 |          1        0.03       98.44
       4188 |          1        0.03       98.47
       4189 |          1        0.03       98.51
       4194 |          1        0.03       98.54
       4195 |          1        0.03       98.57
       4196 |          1        0.03       98.60
       4197 |          1        0.03       98.63
       4202 |          1        0.03       98.66
       4220 |          1        0.03       98.69
       4221 |          1        0.03       98.72
       4226 |          1        0.03       98.75
       4231 |          1        0.03       98.78
       4232 |          1        0.03       98.81
       4234 |          1        0.03       98.84
       4238 |          1        0.03       98.87
       4239 |          1        0.03       98.90
       4241 |          1        0.03       98.93
       4248 |          1        0.03       98.96
       4251 |          1        0.03       98.99
       4254 |          1        0.03       99.02
       4256 |          1        0.03       99.05
       4257 |          1        0.03       99.08
       4258 |          1        0.03       99.12
       4261 |          1        0.03       99.15
       4262 |          1        0.03       99.18
       4263 |          1        0.03       99.21
       4264 |          1        0.03       99.24
       4266 |          1        0.03       99.27
       4267 |          1        0.03       99.30
       4272 |          1        0.03       99.33
       4273 |          1        0.03       99.36
       4287 |          1        0.03       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. sum id

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
          id |      3,258     1992.12    1185.232         10       4287

. replace id = _n+10000 if id==.
(20 real changes made)

. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Dependent variable
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. tab1 masn1*, mis

-> tabulation of masn1a  

            Schließung Kitas sollte ... |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        1. 1 - sofort aufgehoben werden |        578       17.63       17.63
                                   2. 2 |        180        5.49       23.12
                                   3. 3 |        338       10.31       33.44
                                   4. 4 |        264        8.05       41.49
                                   5. 5 |        640       19.52       61.01
                                   6. 6 |        291        8.88       69.89
                                   7. 7 |        284        8.66       78.55
                                   8. 8 |        343       10.46       89.02
                                   9. 9 |         94        2.87       91.89
10. 10 - erst aufgehoben werden, wenn e |        244        7.44       99.33
                                      . |         22        0.67      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of masn1b  

           Schließung Schulen sollte... |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        1. 1 - sofort aufgehoben werden |        651       19.86       19.86
                                   2. 2 |        249        7.60       27.46
                                   3. 3 |        393       11.99       39.44
                                   4. 4 |        266        8.11       47.56
                                   5. 5 |        597       18.21       65.77
                                   6. 6 |        245        7.47       73.25
                                   7. 7 |        250        7.63       80.87
                                   8. 8 |        315        9.61       90.48
                                   9. 9 |         86        2.62       93.11
10. 10 - erst aufgehoben werden, wenn e |        195        5.95       99.05
                                      . |         31        0.95      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of masn1c  

      Schließung Gaststätten sollte ... |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        1. 1 - sofort aufgehoben werden |        609       18.58       18.58
                                   2. 2 |        227        6.92       25.50
                                   3. 3 |        403       12.29       37.80
                                   4. 4 |        310        9.46       47.25
                                   5. 5 |        516       15.74       63.00
                                   6. 6 |        250        7.63       70.62
                                   7. 7 |        289        8.82       79.44
                                   8. 8 |        299        9.12       88.56
                                   9. 9 |        114        3.48       92.04
10. 10 - erst aufgehoben werden, wenn e |        237        7.23       99.27
                                      . |         24        0.73      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of masn1d  

    Ausgangs- und Kontaktbeschränkungen |
                            sollten ... |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        1. 1 - sofort aufgehoben werden |        551       16.81       16.81
                                   2. 2 |        230        7.02       23.83
                                   3. 3 |        395       12.05       35.88
                                   4. 4 |        310        9.46       45.33
                                   5. 5 |        562       17.14       62.48
                                   6. 6 |        264        8.05       70.53
                                   7. 7 |        320        9.76       80.29
                                   8. 8 |        324        9.88       90.18
                                   9. 9 |        119        3.63       93.81
10. 10 - erst aufgehoben werden, wenn e |        180        5.49       99.30
                                      . |         23        0.70      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of masn1e  

    Grenzschließungen innerhalb Europas |
                            sollten ... |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        1. 1 - sofort aufgehoben werden |        509       15.53       15.53
                                   2. 2 |        208        6.35       21.87
                                   3. 3 |        299        9.12       30.99
                                   4. 4 |        275        8.39       39.38
                                   5. 5 |        545       16.63       56.01
                                   6. 6 |        230        7.02       63.03
                                   7. 7 |        274        8.36       71.38
                                   8. 8 |        306        9.33       80.72
                                   9. 9 |        169        5.16       85.88
10. 10 - erst aufgehoben werden, wenn e |        432       13.18       99.05
                                      . |         31        0.95      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of masn1f  

     Absage von Großveranstaltungen wie |
 Festen und Sportveranstaltungen sollte |
                                    ... |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        1. 1 - sofort aufgehoben werden |        226        6.89        6.89
                                   2. 2 |         76        2.32        9.21
                                   3. 3 |        203        6.19       15.41
                                   4. 4 |        173        5.28       20.68
                                   5. 5 |        426       13.00       33.68
                                   6. 6 |        214        6.53       40.21
                                   7. 7 |        303        9.24       49.45
                                   8. 8 |        469       14.31       63.76
                                   9. 9 |        331       10.10       73.86
10. 10 - erst aufgehoben werden, wenn e |        836       25.50       99.36
                                      . |         21        0.64      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

. 
. foreach x of varlist masn1* {
  2.         gen `x'rec = 11-`x'
  3. }
(22 missing values generated)
(31 missing values generated)
(24 missing values generated)
(23 missing values generated)
(31 missing values generated)
(21 missing values generated)

. tab1 *rec, mis

-> tabulation of masn1arec  

  masn1arec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        244        7.44        7.44
          2 |         94        2.87       10.31
          3 |        343       10.46       20.77
          4 |        284        8.66       29.44
          5 |        291        8.88       38.32
          6 |        640       19.52       57.84
          7 |        264        8.05       65.89
          8 |        338       10.31       76.21
          9 |        180        5.49       81.70
         10 |        578       17.63       99.33
          . |         22        0.67      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of masn1brec  

  masn1brec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        195        5.95        5.95
          2 |         86        2.62        8.57
          3 |        315        9.61       18.18
          4 |        250        7.63       25.81
          5 |        245        7.47       33.28
          6 |        597       18.21       51.49
          7 |        266        8.11       59.61
          8 |        393       11.99       71.60
          9 |        249        7.60       79.19
         10 |        651       19.86       99.05
          . |         31        0.95      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of masn1crec  

  masn1crec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        237        7.23        7.23
          2 |        114        3.48       10.71
          3 |        299        9.12       19.83
          4 |        289        8.82       28.65
          5 |        250        7.63       36.27
          6 |        516       15.74       52.01
          7 |        310        9.46       61.47
          8 |        403       12.29       73.76
          9 |        227        6.92       80.69
         10 |        609       18.58       99.27
          . |         24        0.73      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of masn1drec  

  masn1drec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        180        5.49        5.49
          2 |        119        3.63        9.12
          3 |        324        9.88       19.01
          4 |        320        9.76       28.77
          5 |        264        8.05       36.82
          6 |        562       17.14       53.97
          7 |        310        9.46       63.42
          8 |        395       12.05       75.47
          9 |        230        7.02       82.49
         10 |        551       16.81       99.30
          . |         23        0.70      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of masn1erec  

  masn1erec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        432       13.18       13.18
          2 |        169        5.16       18.33
          3 |        306        9.33       27.67
          4 |        274        8.36       36.03
          5 |        230        7.02       43.04
          6 |        545       16.63       59.67
          7 |        275        8.39       68.06
          8 |        299        9.12       77.18
          9 |        208        6.35       83.53
         10 |        509       15.53       99.05
          . |         31        0.95      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of masn1frec  

  masn1frec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        836       25.50       25.50
          2 |        331       10.10       35.60
          3 |        469       14.31       49.91
          4 |        303        9.24       59.15
          5 |        214        6.53       65.68
          6 |        426       13.00       78.68
          7 |        173        5.28       83.95
          8 |        203        6.19       90.15
          9 |         76        2.32       92.46
         10 |        226        6.89       99.36
          . |         21        0.64      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. rename masn1arec openkitas

. rename masn1brec openschools

. rename masn1crec openrestau

. rename masn1drec opencontact

. rename masn1erec openborders

. rename masn1frec openevents

. 
. pwcorr open*, sig

             | openki~s opensc~s openre~u openco~t openbo~s openev~s
-------------+------------------------------------------------------
   openkitas |   1.0000 
             |
             |
 openschools |   0.8673   1.0000 
             |   0.0000
             |
  openrestau |   0.5902   0.6124   1.0000 
             |   0.0000   0.0000
             |
 opencontact |   0.5237   0.5615   0.6664   1.0000 
             |   0.0000   0.0000   0.0000
             |
 openborders |   0.3682   0.3758   0.4743   0.4952   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
  openevents |   0.4066   0.3961   0.5052   0.5522   0.4927   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |

. factor open* [aweight=gewicht], pcf blanks(.4)
(sum of wgt is 3,226.579)
(obs=3,228)

Factor analysis/correlation                      Number of obs    =      3,228
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          6

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      3.59753      2.68498            0.5996       0.5996
        Factor2  |      0.91254      0.38426            0.1521       0.7517
        Factor3  |      0.52829      0.04593            0.0880       0.8397
        Factor4  |      0.48236      0.14225            0.0804       0.9201
        Factor5  |      0.34011      0.20093            0.0567       0.9768
        Factor6  |      0.13918            .            0.0232       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(15) = 1.0e+04 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
       openkitas |   0.8088 |      0.3459  
     openschools |   0.8263 |      0.3172  
      openrestau |   0.8229 |      0.3229  
     opencontact |   0.8117 |      0.3412  
     openborders |   0.6686 |      0.5530  
      openevents |   0.6912 |      0.5223  
    ---------------------------------------
    (blanks represent abs(loading)<.4)

. 
. factor open* [aweight=gewicht], pcf blanks(.4) mineigen(.89)
(sum of wgt is 3,226.579)
(obs=3,228)

Factor analysis/correlation                      Number of obs    =      3,228
    Method: principal-component factors          Retained factors =          2
    Rotation: (unrotated)                        Number of params =         11

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      3.59753      2.68498            0.5996       0.5996
        Factor2  |      0.91254      0.38426            0.1521       0.7517
        Factor3  |      0.52829      0.04593            0.0880       0.8397
        Factor4  |      0.48236      0.14225            0.0804       0.9201
        Factor5  |      0.34011      0.20093            0.0567       0.9768
        Factor6  |      0.13918            .            0.0232       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(15) = 1.0e+04 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
       openkitas |   0.8088   -0.4884 |      0.1073  
     openschools |   0.8263   -0.4743 |      0.0922  
      openrestau |   0.8229           |      0.3220  
     opencontact |   0.8117           |      0.3106  
     openborders |   0.6686    0.4646 |      0.3372  
      openevents |   0.6912    0.4491 |      0.3206  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

. rotate, oblique oblimin blanks(.4)

Factor analysis/correlation                      Number of obs    =      3,228
    Method: principal-component factors          Retained factors =          2
    Rotation: oblique oblimin (Kaiser off)       Number of params =         11

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      2.94364       0.4906
        Factor2  |      2.92011       0.4867
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(15) = 1.0e+04 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
       openkitas |             0.9541 |      0.1073  
     openschools |             0.9499 |      0.0922  
      openrestau |   0.5079    0.4408 |      0.3220  
     opencontact |   0.6472           |      0.3106  
     openborders |   0.8541           |      0.3372  
      openevents |   0.8516           |      0.3206  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

Factor rotation matrix

    --------------------------------
                 | Factor1  Factor2 
    -------------+------------------
         Factor1 |  0.8698   0.8647 
         Factor2 |  0.4935  -0.5023 
    --------------------------------

. 
. alpha open*, item detail

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
openkitas    | 3256    +       0.8006        0.7011        4.011006      0.8389
openschools  | 3247    +       0.8129        0.7191        3.976852      0.8360
openrestau   | 3254    +       0.8237        0.7319        3.906043      0.8333
opencontact  | 3255    +       0.8113        0.7188        4.004059      0.8361
openborders  | 3247    +       0.6970        0.5464        4.305888      0.8673
openevents   | 3257    +       0.7216        0.5887        4.263545      0.8586
-------------+-----------------------------------------------------------------
Test scale   |                                              4.07791      0.8677
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

               openkitas  openschools   openrestau  opencontact  openborders   openevents
  openkitas       7.5493
openschools       6.5033       7.4638
 openrestau       4.5234       4.6646       7.7848
opencontact       3.8663       4.1204       4.9933       7.2129
openborders       3.0098       3.0525       3.9419       3.9550       8.8613
 openevents       3.1577       3.0579       3.9840       4.1919       4.1443       7.9856

Pairwise number of observations

               openkitas  openschools   openrestau  opencontact  openborders   openevents
  openkitas         3256
openschools         3246         3247
 openrestau         3252         3243         3254
opencontact         3253         3244         3251         3255
openborders         3245         3236         3244         3244         3247
 openevents         3255         3246         3253         3254         3246         3257

. 
. *Generate index var of all 6 items
. egen openindex = rowmean(open*)
(20 missing values generated)

. tab openindex, mis

  openindex |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         51        1.56        1.56
   1.166667 |          6        0.18        1.74
   1.333333 |          9        0.27        2.01
        1.5 |          9        0.27        2.29
   1.666667 |         20        0.61        2.90
   1.833333 |         29        0.88        3.78
          2 |         24        0.73        4.51
   2.166667 |         24        0.73        5.25
        2.2 |          1        0.03        5.28
   2.333333 |         32        0.98        6.25
        2.5 |         36        1.10        7.35
        2.6 |          4        0.12        7.47
   2.666667 |         45        1.37        8.85
        2.8 |          2        0.06        8.91
   2.833333 |         46        1.40       10.31
          3 |         52        1.59       11.90
   3.166667 |         50        1.53       13.42
        3.2 |          2        0.06       13.48
   3.333333 |         55        1.68       15.16
        3.5 |         52        1.59       16.75
        3.6 |          1        0.03       16.78
   3.666667 |         55        1.68       18.46
        3.8 |          2        0.06       18.52
   3.833333 |         62        1.89       20.41
          4 |         82        2.50       22.91
   4.166667 |         61        1.86       24.77
   4.333333 |         62        1.89       26.66
        4.5 |         74        2.26       28.92
        4.6 |          1        0.03       28.95
   4.666667 |         92        2.81       31.76
        4.8 |          2        0.06       31.82
   4.833333 |         83        2.53       34.35
          5 |         98        2.99       37.34
   5.166667 |         93        2.84       40.18
   5.333333 |         95        2.90       43.08
        5.4 |          1        0.03       43.11
        5.5 |        103        3.14       46.25
        5.6 |          1        0.03       46.28
   5.666667 |         82        2.50       48.78
   5.833333 |         80        2.44       51.22
          6 |        115        3.51       54.73
   6.166667 |         70        2.14       56.86
   6.333333 |         90        2.75       59.61
        6.4 |          1        0.03       59.64
        6.5 |         81        2.47       62.11
        6.6 |          1        0.03       62.14
   6.666667 |         96        2.93       65.07
        6.8 |          1        0.03       65.10
   6.833333 |         74        2.26       67.36
          7 |         89        2.72       70.07
   7.166667 |         79        2.41       72.48
   7.333333 |         77        2.35       74.83
        7.4 |          1        0.03       74.86
        7.5 |         69        2.10       76.97
   7.666667 |         76        2.32       79.29
   7.833333 |         78        2.38       81.67
          8 |         68        2.07       83.74
   8.166667 |         42        1.28       85.02
        8.2 |          1        0.03       85.05
   8.333333 |         57        1.74       86.79
        8.4 |          1        0.03       86.82
        8.5 |         56        1.71       88.53
   8.666667 |         45        1.37       89.90
   8.833333 |         31        0.95       90.85
          9 |         32        0.98       91.82
   9.166667 |         26        0.79       92.62
        9.2 |          1        0.03       92.65
   9.333333 |         47        1.43       94.08
        9.5 |         19        0.58       94.66
        9.6 |          1        0.03       94.69
   9.666667 |         21        0.64       95.33
   9.833333 |         11        0.34       95.67
         10 |        122        3.72       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *z-standardize dependent variable
. sum openindex [aweight=gewicht]

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   openindex |   3,258    3257.976    5.687714    2.13141          1         10

. center openindex [aweight=gewicht], gen(zopenindex) double nolabel standardize
(generated variables: zopenindex)

. center openindex , gen(znowopenindex) double nolabel standardize
(generated variables: znowopenindex)

. 
. *Re-scale to five-point scale
. tab1 openkitas-openevents, mis

-> tabulation of openkitas  

  openkitas |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        244        7.44        7.44
          2 |         94        2.87       10.31
          3 |        343       10.46       20.77
          4 |        284        8.66       29.44
          5 |        291        8.88       38.32
          6 |        640       19.52       57.84
          7 |        264        8.05       65.89
          8 |        338       10.31       76.21
          9 |        180        5.49       81.70
         10 |        578       17.63       99.33
          . |         22        0.67      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openschools  

openschools |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        195        5.95        5.95
          2 |         86        2.62        8.57
          3 |        315        9.61       18.18
          4 |        250        7.63       25.81
          5 |        245        7.47       33.28
          6 |        597       18.21       51.49
          7 |        266        8.11       59.61
          8 |        393       11.99       71.60
          9 |        249        7.60       79.19
         10 |        651       19.86       99.05
          . |         31        0.95      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openrestau  

 openrestau |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        237        7.23        7.23
          2 |        114        3.48       10.71
          3 |        299        9.12       19.83
          4 |        289        8.82       28.65
          5 |        250        7.63       36.27
          6 |        516       15.74       52.01
          7 |        310        9.46       61.47
          8 |        403       12.29       73.76
          9 |        227        6.92       80.69
         10 |        609       18.58       99.27
          . |         24        0.73      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of opencontact  

opencontact |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        180        5.49        5.49
          2 |        119        3.63        9.12
          3 |        324        9.88       19.01
          4 |        320        9.76       28.77
          5 |        264        8.05       36.82
          6 |        562       17.14       53.97
          7 |        310        9.46       63.42
          8 |        395       12.05       75.47
          9 |        230        7.02       82.49
         10 |        551       16.81       99.30
          . |         23        0.70      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openborders  

openborders |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        432       13.18       13.18
          2 |        169        5.16       18.33
          3 |        306        9.33       27.67
          4 |        274        8.36       36.03
          5 |        230        7.02       43.04
          6 |        545       16.63       59.67
          7 |        275        8.39       68.06
          8 |        299        9.12       77.18
          9 |        208        6.35       83.53
         10 |        509       15.53       99.05
          . |         31        0.95      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openevents  

 openevents |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        836       25.50       25.50
          2 |        331       10.10       35.60
          3 |        469       14.31       49.91
          4 |        303        9.24       59.15
          5 |        214        6.53       65.68
          6 |        426       13.00       78.68
          7 |        173        5.28       83.95
          8 |        203        6.19       90.15
          9 |         76        2.32       92.46
         10 |        226        6.89       99.36
          . |         21        0.64      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. foreach x of varlist openkitas-openevents{
  2.    recode `x' (1 2=1) (3 4=2) (5 6=3) (7 8=4) (9 10=5), gen(`x'5)
  3. }
(3012 differences between openkitas and openkitas5)
(3052 differences between openschools and openschools5)
(3017 differences between openrestau and openrestau5)
(3075 differences between opencontact and opencontact5)
(2815 differences between openborders and openborders5)
(2421 differences between openevents and openevents5)

. tab1 open*5, mis

-> tabulation of openkitas5  

  RECODE of |
  openkitas |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        338       10.31       10.31
          2 |        627       19.13       29.44
          3 |        931       28.40       57.84
          4 |        602       18.36       76.21
          5 |        758       23.12       99.33
          . |         22        0.67      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openschools5  

  RECODE of |
openschools |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        281        8.57        8.57
          2 |        565       17.24       25.81
          3 |        842       25.69       51.49
          4 |        659       20.10       71.60
          5 |        900       27.46       99.05
          . |         31        0.95      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openrestau5  

  RECODE of |
 openrestau |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        351       10.71       10.71
          2 |        588       17.94       28.65
          3 |        766       23.37       52.01
          4 |        713       21.75       73.76
          5 |        836       25.50       99.27
          . |         24        0.73      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of opencontact5  

  RECODE of |
opencontact |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        299        9.12        9.12
          2 |        644       19.65       28.77
          3 |        826       25.20       53.97
          4 |        705       21.51       75.47
          5 |        781       23.83       99.30
          . |         23        0.70      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openborders5  

  RECODE of |
openborders |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        601       18.33       18.33
          2 |        580       17.69       36.03
          3 |        775       23.64       59.67
          4 |        574       17.51       77.18
          5 |        717       21.87       99.05
          . |         31        0.95      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openevents5  

  RECODE of |
 openevents |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,167       35.60       35.60
          2 |        772       23.55       59.15
          3 |        640       19.52       78.68
          4 |        376       11.47       90.15
          5 |        302        9.21       99.36
          . |         21        0.64      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. egen openindex5 = rowmean(open*5)
(20 missing values generated)

. tab openindex5, mis

 openindex5 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         72        2.20        2.20
   1.166667 |         32        0.98        3.17
   1.333333 |         58        1.77        4.94
        1.5 |         61        1.86        6.80
        1.6 |          3        0.09        6.89
   1.666667 |         98        2.99        9.88
        1.8 |          5        0.15       10.04
   1.833333 |         97        2.96       13.00
          2 |        142        4.33       17.33
   2.166667 |        123        3.75       21.08
        2.2 |          1        0.03       21.11
   2.333333 |        154        4.70       25.81
        2.5 |        175        5.34       31.15
        2.6 |          3        0.09       31.24
   2.666667 |        188        5.74       36.97
   2.833333 |        187        5.70       42.68
          3 |        225        6.86       49.54
   3.166667 |        164        5.00       54.55
        3.2 |          1        0.03       54.58
   3.333333 |        181        5.52       60.10
        3.4 |          1        0.03       60.13
        3.5 |        165        5.03       65.16
        3.6 |          2        0.06       65.22
   3.666667 |        171        5.22       70.44
       3.75 |          1        0.03       70.47
        3.8 |          1        0.03       70.50
   3.833333 |        157        4.79       75.29
          4 |        144        4.39       79.68
   4.166667 |        141        4.30       83.98
        4.2 |          1        0.03       84.01
   4.333333 |        128        3.90       87.92
        4.4 |          1        0.03       87.95
        4.5 |         87        2.65       90.60
        4.6 |          2        0.06       90.67
   4.666667 |         87        2.65       93.32
        4.8 |          1        0.03       93.35
   4.833333 |         49        1.49       94.84
          5 |        149        4.55       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *z-standardize openkitas and openschools for robustness analysis
. center openkitas [aweight=gewicht], gen(zopenkitas) double nolabel standardize
(generated variables: zopenkitas)

. center openschools [aweight=gewicht], gen(zopenschools) double nolabel standardize
(generated variables: zopenschools)

. 
. *Create opposition dummies for descriptive analysis
. foreach x of varlist openkitas5-openevents5 {
  2.     recode `x' (1/3=0) (4 5=1), gen(`x'dum)
  3. }
(3256 differences between openkitas5 and openkitas5dum)
(3247 differences between openschools5 and openschools5dum)
(3254 differences between openrestau5 and openrestau5dum)
(3255 differences between opencontact5 and opencontact5dum)
(3247 differences between openborders5 and openborders5dum)
(3257 differences between openevents5 and openevents5dum)

. tab1 *dum, mis

-> tabulation of openkitas5dum  

  RECODE of |
 openkitas5 |
 (RECODE of |
 openkitas) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,896       57.84       57.84
          1 |      1,360       41.49       99.33
          . |         22        0.67      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openschools5dum  

  RECODE of |
openschools |
  5 (RECODE |
         of |
openschools |
          ) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,688       51.49       51.49
          1 |      1,559       47.56       99.05
          . |         31        0.95      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openrestau5dum  

  RECODE of |
openrestau5 |
 (RECODE of |
openrestau) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,705       52.01       52.01
          1 |      1,549       47.25       99.27
          . |         24        0.73      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of opencontact5dum  

  RECODE of |
opencontact |
  5 (RECODE |
         of |
opencontact |
          ) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,769       53.97       53.97
          1 |      1,486       45.33       99.30
          . |         23        0.70      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openborders5dum  

  RECODE of |
openborders |
  5 (RECODE |
         of |
openborders |
          ) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,956       59.67       59.67
          1 |      1,291       39.38       99.05
          . |         31        0.95      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of openevents5dum  

  RECODE of |
openevents5 |
 (RECODE of |
openevents) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,579       78.68       78.68
          1 |        678       20.68       99.36
          . |         21        0.64      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Independent variables
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Threats
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Sociotropic threat
. tab1 masn2a-masn2h, mis

-> tabulation of masn2a  

      Maßnahmen Bewältigung |
                    Krise - |
  Arbeitsplatzsicherheit in |
            Dtld insgesamt? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |         82        2.50        2.50
                       2. 2 |        167        5.09        7.60
                       3. 3 |        933       28.46       36.06
                       4. 4 |      1,186       36.18       72.24
     5. 5 - sehr bedrohlich |        884       26.97       99.21
                          . |         26        0.79      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2b  

      Maßnahmen Bewältigung |
        Krise - finanzielle |
          Situation in Dtld |
                 insgesamt? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |         55        1.68        1.68
                       2. 2 |        186        5.67        7.35
                       3. 3 |        921       28.10       35.45
                       4. 4 |      1,183       36.09       71.54
     5. 5 - sehr bedrohlich |        898       27.39       98.93
                          . |         35        1.07      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2c  

      Maßnahmen Bewältigung |
      Krise - Situation der |
Familien in Dtld insgesamt? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |         61        1.86        1.86
                       2. 2 |        301        9.18       11.04
                       3. 3 |      1,200       36.61       47.65
                       4. 4 |      1,090       33.25       80.90
     5. 5 - sehr bedrohlich |        579       17.66       98.57
                          . |         47        1.43      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2d  

      Maßnahmen Bewältigung |
Krise - Grundrechte in Dtld |
                 insgesamt? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |        598       18.24       18.24
                       2. 2 |        712       21.72       39.96
                       3. 3 |        855       26.08       66.05
                       4. 4 |        545       16.63       82.67
     5. 5 - sehr bedrohlich |        536       16.35       99.02
                          . |         32        0.98      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2e  

      Maßnahmen Bewältigung |
 Krise - Sicherheit eigener |
              Arbeitsplatz? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |      1,381       42.13       42.13
                       2. 2 |        608       18.55       60.68
                       3. 3 |        662       20.20       80.87
                       4. 4 |        297        9.06       89.93
     5. 5 - sehr bedrohlich |        267        8.15       98.08
                          . |         63        1.92      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2f  

      Maßnahmen Bewältigung |
 Krise - eigene finanzielle |
                 Situation? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |        860       26.24       26.24
                       2. 2 |        813       24.80       51.04
                       3. 3 |        878       26.78       77.82
                       4. 4 |        424       12.93       90.76
     5. 5 - sehr bedrohlich |        262        7.99       98.75
                          . |         41        1.25      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2g  

      Maßnahmen Bewältigung |
   Krise - eigene familiäre |
                Situation ? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |      1,274       38.87       38.87
                       2. 2 |        892       27.21       66.08
                       3. 3 |        741       22.61       88.68
                       4. 4 |        210        6.41       95.09
     5. 5 - sehr bedrohlich |        117        3.57       98.66
                          . |         44        1.34      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2h  

      Maßnahmen Bewältigung |
Krise - eigene Grundrechte? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |        906       27.64       27.64
                       2. 2 |        767       23.40       51.04
                       3. 3 |        802       24.47       75.50
                       4. 4 |        399       12.17       87.68
     5. 5 - sehr bedrohlich |        374       11.41       99.08
                          . |         30        0.92      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

. gen double sttwork = masn2a
(26 missing values generated)

. gen double sttfinance = masn2b
(35 missing values generated)

. gen double sttfamily = masn2c
(47 missing values generated)

. gen double sttrights = masn2d
(32 missing values generated)

. 
. pwcorr stt*, sig

             |  sttwork sttfin~e sttfam~y sttrig~s
-------------+------------------------------------
     sttwork |   1.0000 
             |
             |
  sttfinance |   0.6039   1.0000 
             |   0.0000
             |
   sttfamily |   0.4631   0.5303   1.0000 
             |   0.0000   0.0000
             |
   sttrights |   0.2855   0.3534   0.3896   1.0000 
             |   0.0000   0.0000   0.0000
             |

. factor stt* [aweight=gewicht], pcf blanks(.4)
(sum of wgt is 3,200.591)
(obs=3,203)

Factor analysis/correlation                      Number of obs    =      3,203
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          4

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.30626      1.53023            0.5766       0.5766
        Factor2  |      0.77603      0.24715            0.1940       0.7706
        Factor3  |      0.52888      0.14005            0.1322       0.9028
        Factor4  |      0.38883            .            0.0972       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(6)  = 3199.37 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
         sttwork |   0.7856 |      0.3829  
      sttfinance |   0.8364 |      0.3004  
       sttfamily |   0.7859 |      0.3823  
       sttrights |   0.6098 |      0.6281  
    ---------------------------------------
    (blanks represent abs(loading)<.4)

. alpha stt*, item detail

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
sttwork      | 3252    +       0.7462        0.5489        .4756135      0.6636
sttfinance   | 3243    +       0.7925        0.6265        .4321007      0.6243
sttfamily    | 3231    +       0.7625        0.5824        .4618706      0.6479
sttrights    | 3246    +       0.7297        0.4116        .4922773      0.7738
-------------+-----------------------------------------------------------------
Test scale   |                                             .4654525      0.7348
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

               sttwork  sttfinance   sttfamily   sttrights
   sttwork      0.9540
sttfinance      0.5639      0.9134
 sttfamily      0.4302      0.4824      0.9041
 sttrights      0.3718      0.4499      0.4946      1.7772

Pairwise number of observations

               sttwork  sttfinance   sttfamily   sttrights
   sttwork        3252
sttfinance        3237        3243
 sttfamily        3226        3217        3231
 sttrights        3241        3231        3222        3246

. 
. *Create index variable
. egen stt = rowmean(stt*)
(20 missing values generated)

. tab stt, mis

        stt |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         12        0.37        0.37
       1.25 |          5        0.15        0.52
        1.5 |          7        0.21        0.73
   1.666667 |          1        0.03        0.76
       1.75 |         34        1.04        1.80
          2 |         72        2.20        4.00
       2.25 |         97        2.96        6.96
   2.333333 |          3        0.09        7.05
        2.5 |        145        4.42       11.47
   2.666667 |          2        0.06       11.53
       2.75 |        268        8.18       19.71
          3 |        413       12.60       32.31
       3.25 |        377       11.50       43.81
   3.333333 |         11        0.34       44.14
        3.5 |        373       11.38       55.52
   3.666667 |          9        0.27       55.80
       3.75 |        340       10.37       66.17
          4 |        335       10.22       76.39
       4.25 |        214        6.53       82.92
   4.333333 |          3        0.09       83.01
        4.5 |        207        6.31       89.32
   4.666667 |          1        0.03       89.35
       4.75 |        124        3.78       93.14
          5 |        205        6.25       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Subindizes
. egen sttshort = rowmean(sttwork sttfinance sttfamily)
(20 missing values generated)

. egen sttecon = rowmean(sttwork sttfinance)
(20 missing values generated)

. 
. 
. *Individual threat
. tab1 masn2e-masn2h, mis

-> tabulation of masn2e  

      Maßnahmen Bewältigung |
 Krise - Sicherheit eigener |
              Arbeitsplatz? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |      1,381       42.13       42.13
                       2. 2 |        608       18.55       60.68
                       3. 3 |        662       20.20       80.87
                       4. 4 |        297        9.06       89.93
     5. 5 - sehr bedrohlich |        267        8.15       98.08
                          . |         63        1.92      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2f  

      Maßnahmen Bewältigung |
 Krise - eigene finanzielle |
                 Situation? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |        860       26.24       26.24
                       2. 2 |        813       24.80       51.04
                       3. 3 |        878       26.78       77.82
                       4. 4 |        424       12.93       90.76
     5. 5 - sehr bedrohlich |        262        7.99       98.75
                          . |         41        1.25      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2g  

      Maßnahmen Bewältigung |
   Krise - eigene familiäre |
                Situation ? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |      1,274       38.87       38.87
                       2. 2 |        892       27.21       66.08
                       3. 3 |        741       22.61       88.68
                       4. 4 |        210        6.41       95.09
     5. 5 - sehr bedrohlich |        117        3.57       98.66
                          . |         44        1.34      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of masn2h  

      Maßnahmen Bewältigung |
Krise - eigene Grundrechte? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
1. 1 - gar nicht bedrohlich |        906       27.64       27.64
                       2. 2 |        767       23.40       51.04
                       3. 3 |        802       24.47       75.50
                       4. 4 |        399       12.17       87.68
     5. 5 - sehr bedrohlich |        374       11.41       99.08
                          . |         30        0.92      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

. gen double idtwork = masn2e
(63 missing values generated)

. gen double idtfinance = masn2f
(41 missing values generated)

. gen double idtfamily = masn2g
(44 missing values generated)

. gen double idtrights = masn2h
(30 missing values generated)

. 
. pwcorr idt*, sig

             |  idtwork idtfin~e idtfam~y idtrig~s
-------------+------------------------------------
     idtwork |   1.0000 
             |
             |
  idtfinance |   0.6743   1.0000 
             |   0.0000
             |
   idtfamily |   0.4293   0.5524   1.0000 
             |   0.0000   0.0000
             |
   idtrights |   0.3111   0.4039   0.4397   1.0000 
             |   0.0000   0.0000   0.0000
             |

. factor idt* [aweight=gewicht], pcf blanks(.4)
(sum of wgt is 3,161.778)
(obs=3,171)

Factor analysis/correlation                      Number of obs    =      3,171
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          4

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.42519      1.67396            0.6063       0.6063
        Factor2  |      0.75123      0.22841            0.1878       0.7941
        Factor3  |      0.52282      0.22206            0.1307       0.9248
        Factor4  |      0.30076            .            0.0752       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(6)  = 3961.35 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
         idtwork |   0.7924 |      0.3722  
      idtfinance |   0.8640 |      0.2535  
       idtfamily |   0.7770 |      0.3962  
       idtrights |   0.6687 |      0.5529  
    ---------------------------------------
    (blanks represent abs(loading)<.4)

. alpha idt*, item detail

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
idtwork      | 3215    +       0.7877        0.5857        .6812216      0.7160
idtfinance   | 3237    +       0.8477        0.7026        .5960923      0.6519
idtfamily    | 3234    +       0.7613        0.5905        .7606136      0.7173
idtrights    | 3248    +       0.7065        0.4546        .8173926      0.7873
-------------+-----------------------------------------------------------------
Test scale   |                                             .7137538      0.7744
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

               idtwork  idtfinance   idtfamily   idtrights
   idtwork      1.7087
idtfinance      1.0883      1.5293
 idtfamily      0.6147      0.7493      1.2034
 idtrights      0.5369      0.6581      0.6365      1.7415

Pairwise number of observations

               idtwork  idtfinance   idtfamily   idtrights
   idtwork        3215
idtfinance        3199        3237
 idtfamily        3195        3216        3234
 idtrights        3207        3229        3226        3248

. 
. *Create index variable
. egen idt = rowmean(idt*)
(22 missing values generated)

. tab idt, mis

        idt |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        337       10.28       10.28
       1.25 |        225        6.86       17.14
   1.333333 |          7        0.21       17.36
        1.5 |        309        9.43       26.78
   1.666667 |         10        0.31       27.09
       1.75 |        317        9.67       36.76
          2 |        329       10.04       46.80
       2.25 |        243        7.41       54.21
   2.333333 |          8        0.24       54.45
        2.5 |        277        8.45       62.90
   2.666667 |          6        0.18       63.09
       2.75 |        239        7.29       70.38
          3 |        316        9.64       80.02
       3.25 |        160        4.88       84.90
   3.333333 |          7        0.21       85.11
        3.5 |        125        3.81       88.93
   3.666667 |          5        0.15       89.08
       3.75 |         98        2.99       92.07
          4 |         90        2.75       94.81
       4.25 |         35        1.07       95.88
   4.333333 |          3        0.09       95.97
        4.5 |         31        0.95       96.92
   4.666667 |          1        0.03       96.95
       4.75 |         19        0.58       97.53
          5 |         59        1.80       99.33
          . |         22        0.67      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Subindizes
. egen idtshort = rowmean(idtwork idtfinance idtfamily)
(23 missing values generated)

. egen idtecon = rowmean(idtwork idtfinance)
(25 missing values generated)

. 
. *Objective change in income due to corona
. tab1 erw8b erw16, mis

-> tabulation of erw8b  

   Wie hat sich Ihr Beitrag |
zum Haushaltseinkommen seit |
           der Corona-Krise |
                 verändert? |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
          1. stark gesunken |        182        5.55        5.55
          2. etwas gesunken |        323        9.85       15.41
3. in etwa gleich geblieben |      1,546       47.16       62.57
         4. etwas gestiegen |         56        1.71       64.28
         5. stark gestiegen |         18        0.55       64.83
                          . |        275        8.39       73.22
                         .f |        878       26.78      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

-> tabulation of erw16  

  Haben Sie |
  wegen der |
Corona-Kris |
    e einen |
Einkommensv |
     erlust |
  hinnehmen |
    müssen? |      Freq.     Percent        Cum.
------------+-----------------------------------
    0. Nein |      1,191       36.33       36.33
      1. Ja |        466       14.22       50.55
          . |         32        0.98       51.53
         .f |      1,589       48.47      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. gen double incloss = erw16==1 if erw16~=.
(32 missing values generated)

. *replace incloss=0 if(erw2==4 |erw2==6) //Beamte and MFA have no loss
. *replace incloss=0 if erw2==5
. replace incloss=1 if(erw2==5 & (erw19==3 | erw19==4))
(57 real changes made)

. *replace incloss=0 if(erw1==8 | erw1==10) // Rentner and unemployed have no loss
. tab incloss, mis

    incloss |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,723       83.07       83.07
          1 |        523       15.95       99.02
          . |         32        0.98      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Political attitudes
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Left-Right-self positioning
. tab inst4, mis  //too much missings, don't use this variable!

      Politische |
Selbsteinstufung |
         'Links' |
       'Rechts'' |      Freq.     Percent        Cum.
-----------------+-----------------------------------
     0. 0(links) |         85        2.59        2.59
            1. 1 |         57        1.74        4.33
            2. 2 |        172        5.25        9.58
            3. 3 |        302        9.21       18.79
            4. 4 |        285        8.69       27.49
            5. 5 |        905       27.61       55.09
            6. 6 |        305        9.30       64.40
            7. 7 |        212        6.47       70.87
            8. 8 |        119        3.63       74.50
            9. 9 |         27        0.82       75.32
  10. 10(rechts) |         51        1.56       76.88
  11. weiß nicht |        327        9.98       86.85
12. keine Angabe |        403       12.29       99.15
               . |         28        0.85      100.00
-----------------+-----------------------------------
           Total |      3,278      100.00

. recast double inst4

. recode inst4 (11 12 .= .), gen(leftright)
(730 differences between inst4 and leftright)

. label var leftright "Left-Right-Self-Assess"

. tab leftright, mis

Left-Right- |
Self-Assess |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         85        2.59        2.59
          1 |         57        1.74        4.33
          2 |        172        5.25        9.58
          3 |        302        9.21       18.79
          4 |        285        8.69       27.49
          5 |        905       27.61       55.09
          6 |        305        9.30       64.40
          7 |        212        6.47       70.87
          8 |        119        3.63       74.50
          9 |         27        0.82       75.32
         10 |         51        1.56       76.88
          . |        758       23.12      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. gen double leftrightDK = inst4==11 if inst4~=.
(28 missing values generated)

. gen double leftrightNR = inst4==12 if inst4~=.
(28 missing values generated)

. gen double leftrightORIG = inst4
(28 missing values generated)

. tab1 leftrightDK leftrightNR, mis

-> tabulation of leftrightDK  

leftrightDK |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,923       89.17       89.17
          1 |        327        9.98       99.15
          . |         28        0.85      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of leftrightNR  

leftrightNR |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,847       86.85       86.85
          1 |        403       12.29       99.15
          . |         28        0.85      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. recode inst4 (0/3=1) (4/6=2) (7/10=3) (11/12=4), gen(leftrightcat)
(3193 differences between inst4 and leftrightcat)

. label variable leftrightcat "Left-Right categorical"

. label define lericat 1 "left" 2 "mid" 3 "right" 4 "DK and NR"

. label value leftrightcat lericat

. numlabel lericat, add

. tab leftrightcat, mis

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |        616       18.79       18.79
      2. mid |      1,495       45.61       64.40
    3. right |        409       12.48       76.88
4. DK and NR |        730       22.27       99.15
           . |         28        0.85      100.00
-------------+-----------------------------------
       Total |      3,278      100.00

. 
. *Party preference
. tab1 inst6*, mis

-> tabulation of inst6  

        Politische Neigung |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
              1. CDU / CSU |        650       19.83       19.83
                    2. SPD |        442       13.48       33.31
                    3. FDP |        121        3.69       37.00
              4. Die Linke |        202        6.16       43.17
                    5. AfD |        251        7.66       50.82
6. Bündnis 90 - Die Grünen |        272        8.30       59.12
          7. Keiner Partei |        572       17.45       76.57
                         8 |         61        1.86       78.43
           9. Keine Angabe |        673       20.53       98.96
                         . |         34        1.04      100.00
---------------------------+-----------------------------------
                     Total |      3,278      100.00

-> tabulation of inst6_other  

    Politische Neigung - |
               Sonstiges |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
                         |         20        0.61        0.61
                 .FILTER |      3,197       97.53       98.14
    Bibeltreue Christen. |          1        0.03       98.17
          Bildungspartei |          1        0.03       98.20
               Bündnis C |          1        0.03       98.23
              Die PARTEI |          2        0.06       98.29
              Die Partei |         12        0.37       98.66
             Die Piraten |          1        0.03       98.69
Die Rechte.  Der UNI Weg |          1        0.03       98.72
           Eigene Partei |          1        0.03       98.75
                      FW |          1        0.03       98.78
            Freie Wähler |          7        0.21       98.99
              Humanisten |          1        0.03       99.02
                   Keine |          1        0.03       99.05
                    MLPD |          1        0.03       99.08
                   NSDAP |          1        0.03       99.12
                  PARTEI |          1        0.03       99.15
                     PBC |          1        0.03       99.18
                 Piraten |          4        0.12       99.30
          Piraten Partei |          3        0.09       99.39
           Piratenpartei |          1        0.03       99.42
              Tierpartei |          2        0.06       99.48
              Tierschutz |          1        0.03       99.51
       Tierschutz Partei |          1        0.03       99.54
        Tierschutzpartei |          3        0.09       99.63
         Widerstand 2020 |          1        0.03       99.66
          Widerstand2020 |          1        0.03       99.69
  Widerstand2020 und ÖPD |          1        0.03       99.73
              die Grauen |          1        0.03       99.76
              die partei |          1        0.03       99.79
           gemäßigte AfD |          1        0.03       99.82
                 piraten |          1        0.03       99.85
        tierschutzpartei |          1        0.03       99.88
                     ÖDP |          3        0.09       99.97
                 Ökolinx |          1        0.03      100.00
-------------------------+-----------------------------------
                   Total |      3,278      100.00

. gen other = inst6_other~=".FILTER"

. tab other, mis

      other |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,197       97.53       97.53
          1 |         81        2.47      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. replace inst6 = 7 if other==1
(81 real changes made)

. gen double partypref = inst6
(14 missing values generated)

. label define inst5b 7 "keine/andere Partei", modify

. label value partypref inst5b

. tab partypref, mis

                 partypref |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
              1. CDU / CSU |        650       19.83       19.83
                    2. SPD |        442       13.48       33.31
                    3. FDP |        121        3.69       37.00
              4. Die Linke |        202        6.16       43.17
                    5. AfD |        251        7.66       50.82
6. Bündnis 90 - Die Grünen |        272        8.30       59.12
       keine/andere Partei |        653       19.92       79.04
           9. Keine Angabe |        673       20.53       99.57
                         . |         14        0.43      100.00
---------------------------+-----------------------------------
                     Total |      3,278      100.00

. drop other

. 
. 
. *Trust in institutions
. tab1 inst1a, mis

-> tabulation of inst1a  

      Vertrauen Institutionen - |
                Bundesregierung |      Freq.     Percent        Cum.
--------------------------------+-----------------------------------
1. 1 - überhaupt kein Vertrauen |        341       10.40       10.40
                           2. 2 |        248        7.57       17.97
                           3. 3 |        408       12.45       30.41
                           4. 4 |        517       15.77       46.19
                           5. 5 |        732       22.33       68.52
                           6. 6 |        673       20.53       89.05
   7. 7 - sehr großes Vertrauen |        332       10.13       99.18
                              . |         27        0.82      100.00
--------------------------------+-----------------------------------
                          Total |      3,278      100.00

. rename inst1a trustbundesreg

. rename inst1b trustbundestag

. rename inst1c trustlandreg

. rename inst1d trustparties

. rename inst1e trusttelly

. rename inst1f trustjournals

. rename inst1g trustsocmed

. rename inst1h trusthealthsys

. rename inst1i trustpolice

. rename inst1j trustscience

. 
. recast double trust*

. 
. factor trust* [aweight=gewicht], pcf blanks(.4)
(sum of wgt is 3,147.424)
(obs=3,154)

Factor analysis/correlation                      Number of obs    =      3,154
    Method: principal-component factors          Retained factors =          2
    Rotation: (unrotated)                        Number of params =         19

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      5.82964      4.65749            0.5830       0.5830
        Factor2  |      1.17215      0.35561            0.1172       0.7002
        Factor3  |      0.81654      0.23860            0.0817       0.7818
        Factor4  |      0.57794      0.15558            0.0578       0.8396
        Factor5  |      0.42236      0.04488            0.0422       0.8819
        Factor6  |      0.37749      0.07809            0.0377       0.9196
        Factor7  |      0.29940      0.02300            0.0299       0.9496
        Factor8  |      0.27640      0.14630            0.0276       0.9772
        Factor9  |      0.13010      0.03211            0.0130       0.9902
       Factor10  |      0.09799            .            0.0098       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(45) = 2.4e+04 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
    trustbund~eg |   0.8810           |      0.1887  
    trustbund~ag |   0.8854           |      0.1948  
    trustlandreg |   0.8146           |      0.3146  
    trustparties |   0.8029           |      0.3553  
      trusttelly |   0.7974    0.4193 |      0.1882  
    trustjourn~s |   0.7742    0.4235 |      0.2212  
     trustsocmed |   0.4205    0.7348 |      0.2832  
    trusthealt~s |   0.7381           |      0.3927  
     trustpolice |   0.7051           |      0.4316  
    trustscience |   0.7122           |      0.4278  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

. rotate, oblique oblimin blanks(.4)  //two factors, state+science and media

Factor analysis/correlation                      Number of obs    =      3,154
    Method: principal-component factors          Retained factors =          2
    Rotation: oblique oblimin (Kaiser off)       Number of params =         19

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      5.56980       0.5570
        Factor2  |      3.14617       0.3146
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(45) = 2.4e+04 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
    trustbund~eg |   0.8870           |      0.1887  
    trustbund~ag |   0.8606           |      0.1948  
    trustlandreg |   0.8017           |      0.3146  
    trustparties |   0.6859           |      0.3553  
      trusttelly |             0.6684 |      0.1882  
    trustjourn~s |             0.6669 |      0.2212  
     trustsocmed |             0.9125 |      0.2832  
    trusthealt~s |   0.8112           |      0.3927  
     trustpolice |   0.7954           |      0.4316  
    trustscience |   0.7927           |      0.4278  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

Factor rotation matrix

    --------------------------------
                 | Factor1  Factor2 
    -------------+------------------
         Factor1 |  0.9717   0.6510 
         Factor2 | -0.2362   0.7591 
    --------------------------------

. 
. *Test subdimensions for uni-dimensionality
. factor trustbundes* trustland trustparties trusthealthsys trustpolice trustscience ///
>    [aweight=gewicht] , pcf blanks(.4) //yep
(sum of wgt is 3,175.998)
(obs=3,181)

Factor analysis/correlation                      Number of obs    =      3,181
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          7

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      4.66936      3.83787            0.6671       0.6671
        Factor2  |      0.83150      0.39879            0.1188       0.7858
        Factor3  |      0.43270      0.05677            0.0618       0.8477
        Factor4  |      0.37593      0.06800            0.0537       0.9014
        Factor5  |      0.30793      0.02351            0.0440       0.9453
        Factor6  |      0.28442      0.18627            0.0406       0.9860
        Factor7  |      0.09815            .            0.0140       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(21) = 1.7e+04 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
    trustbund~eg |   0.9069 |      0.1775  
    trustbund~ag |   0.9046 |      0.1818  
    trustlandreg |   0.8419 |      0.2912  
    trustparties |   0.8054 |      0.3513  
    trusthealt~s |   0.7674 |      0.4111  
     trustpolice |   0.7352 |      0.4594  
    trustscience |   0.7359 |      0.4584  
    ---------------------------------------
    (blanks represent abs(loading)<.4)

. 
. factor trusttelly trustjournals trustsocmed ///
>    [aweight=gewicht] , pcf blanks(.4)  //yep
(sum of wgt is 3,217.948)
(obs=3,221)

Factor analysis/correlation                      Number of obs    =      3,221
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          3

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.21352      1.56403            0.7378       0.7378
        Factor2  |      0.64949      0.51250            0.2165       0.9543
        Factor3  |      0.13699            .            0.0457       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(3)  = 5230.56 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
      trusttelly |   0.9294 |      0.1362  
    trustjourn~s |   0.9238 |      0.1466  
     trustsocmed |   0.7045 |      0.5037  
    ---------------------------------------
    (blanks represent abs(loading)<.4)

. 
. *Reliability
. alpha trustbundes* trustland trustparties trusthealthsys trustpolice trustscience, item detail

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
trustbund~eg | 3251    +       0.9046        0.8588        1.540051      0.8934
trustbund~ag | 3243    +       0.8989        0.8537        1.576859      0.8943
trustlandreg | 3232    +       0.8473        0.7799         1.61602      0.9023
trustparties | 3245    +       0.7943        0.7225        1.746118      0.9084
trusthealt~s | 3249    +       0.7676        0.6877        1.771334      0.9119
trustpolice  | 3245    +       0.7556        0.6634        1.746449      0.9144
trustscience | 3252    +       0.7568        0.6676        1.755014      0.9139
-------------+-----------------------------------------------------------------
Test scale   |                                             1.678819      0.9182
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

                trustbundesreg  trustbundestag    trustlandreg    trustparties  trusthealthsys     trustpolice    trustscience
trustbundesreg          3.2235
trustbundestag          2.7698          2.9086
  trustlandreg          2.4459          2.2781          3.0848
  trustparties          1.9373          1.9271          1.8492          2.2652
trusthealthsys          1.6034          1.4975          1.4337          1.1125          2.2864
   trustpolice          1.6937          1.5653          1.5152          1.1297          1.5688          2.7156
  trustscience          1.7036          1.5668          1.4976          1.1095          1.4702          1.5851          2.5955

Pairwise number of observations

                trustbundesreg  trustbundestag    trustlandreg    trustparties  trusthealthsys     trustpolice    trustscience
trustbundesreg            3251
trustbundestag            3237            3243
  trustlandreg            3226            3219            3232
  trustparties            3239            3232            3221            3245
trusthealthsys            3242            3235            3223            3236            3249
   trustpolice            3239            3231            3221            3233            3237            3245
  trustscience            3246            3239            3229            3241            3243            3241            3252

. alpha trusttelly trustjournals trustsocmed, item detail  //social media problematic

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
trusttelly   | 3248    +       0.9208        0.7956        1.065561      0.6253
trustjourn~s | 3245    +       0.9144        0.7861        1.118787      0.6351
trustsocmed  | 3237    +       0.7318        0.4805        2.301981      0.9289
-------------+-----------------------------------------------------------------
Test scale   |                                             1.496324      0.8222
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

                  trusttelly  trustjournals    trustsocmed
   trusttelly         2.7159
trustjournals         2.3020         2.5928
  trustsocmed         1.1188         1.0656         2.0914

Pairwise number of observations

                  trusttelly  trustjournals    trustsocmed
   trusttelly           3248
trustjournals           3239           3245
  trustsocmed           3230           3227           3237

. 
. *Generate index variables
. egen truststate = rowmean(trustbundes* trustland trustparties trusthealthsys trustpolice trustscience)
(20 missing values generated)

. egen trustmedia = rowmean(trusttelly trustjournals trustsocmed)
(23 missing values generated)

. egen trustpress = rowmean(trusttelly trustjournals)
(24 missing values generated)

. tab1 truststate trustmedia trustpress

-> tabulation of truststate  

 truststate |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         48        1.47        1.47
   1.142857 |          8        0.25        1.72
   1.285714 |         17        0.52        2.24
   1.428571 |         23        0.71        2.95
        1.5 |          1        0.03        2.98
   1.571429 |         20        0.61        3.59
   1.714286 |         24        0.74        4.33
   1.857143 |         22        0.68        5.00
          2 |         42        1.29        6.29
   2.142857 |         38        1.17        7.46
   2.166667 |          2        0.06        7.52
   2.285714 |         45        1.38        8.90
   2.333333 |          1        0.03        8.93
   2.428571 |         43        1.32       10.25
        2.5 |          1        0.03       10.28
   2.571429 |         52        1.60       11.88
   2.666667 |          2        0.06       11.94
   2.714286 |         52        1.60       13.54
   2.857143 |         61        1.87       15.41
          3 |         73        2.24       17.65
   3.142857 |         72        2.21       19.86
   3.285714 |         82        2.52       22.38
   3.333333 |          2        0.06       22.44
   3.428571 |         68        2.09       24.52
   3.571429 |         88        2.70       27.23
   3.666667 |          1        0.03       27.26
   3.714286 |         86        2.64       29.90
   3.833333 |          5        0.15       30.05
   3.857143 |         81        2.49       32.54
          4 |        161        4.94       37.48
   4.142857 |        104        3.19       40.67
   4.166667 |          6        0.18       40.85
   4.285714 |        117        3.59       44.44
   4.333333 |          2        0.06       44.51
   4.428571 |        100        3.07       47.58
        4.5 |          3        0.09       47.67
   4.571429 |        119        3.65       51.32
   4.666667 |          4        0.12       51.44
   4.714286 |        111        3.41       54.85
   4.833333 |          3        0.09       54.94
   4.857143 |        126        3.87       58.81
          5 |        151        4.63       63.44
   5.142857 |        144        4.42       67.86
   5.166667 |          8        0.25       68.11
   5.285714 |        130        3.99       72.10
   5.333333 |          3        0.09       72.19
   5.428571 |        134        4.11       76.30
        5.5 |          2        0.06       76.37
   5.571429 |        122        3.74       80.11
   5.666667 |          5        0.15       80.26
   5.714286 |        119        3.65       83.92
   5.833333 |          3        0.09       84.01
   5.857143 |        129        3.96       87.97
          6 |        111        3.41       91.38
   6.142857 |         71        2.18       93.55
   6.166667 |          2        0.06       93.62
   6.285714 |         54        1.66       95.27
   6.333333 |          1        0.03       95.30
        6.4 |          1        0.03       95.33
   6.428571 |         41        1.26       96.59
        6.5 |          2        0.06       96.65
   6.571429 |         39        1.20       97.85
   6.714286 |         21        0.64       98.50
   6.857143 |         15        0.46       98.96
          7 |         34        1.04      100.00
------------+-----------------------------------
      Total |      3,258      100.00

-> tabulation of trustmedia  

 trustmedia |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        272        8.36        8.36
   1.333333 |         67        2.06       10.41
        1.5 |          1        0.03       10.45
   1.666667 |        152        4.67       15.12
          2 |        272        8.36       23.47
   2.333333 |        196        6.02       29.49
        2.5 |          2        0.06       29.55
   2.666667 |        196        6.02       35.58
          3 |        344       10.57       46.14
   3.333333 |        207        6.36       52.50
        3.5 |          7        0.22       52.72
   3.666667 |        237        7.28       60.00
          4 |        407       12.50       72.50
   4.333333 |        255        7.83       80.34
        4.5 |          4        0.12       80.46
   4.666667 |        183        5.62       86.08
          5 |        203        6.24       92.32
   5.333333 |         88        2.70       95.02
   5.666667 |         53        1.63       96.65
          6 |         67        2.06       98.71
   6.333333 |         14        0.43       99.14
        6.5 |          2        0.06       99.20
   6.666667 |          5        0.15       99.35
          7 |         21        0.65      100.00
------------+-----------------------------------
      Total |      3,255      100.00

-> tabulation of trustpress  

 trustpress |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        368       11.31       11.31
        1.5 |         84        2.58       13.89
          2 |        345       10.60       24.49
        2.5 |        165        5.07       29.56
          3 |        447       13.74       43.30
        3.5 |        158        4.86       48.16
          4 |        621       19.08       67.24
        4.5 |        171        5.26       72.50
          5 |        432       13.28       85.77
        5.5 |        134        4.12       89.89
          6 |        229        7.04       96.93
        6.5 |         27        0.83       97.76
          7 |         73        2.24      100.00
------------+-----------------------------------
      Total |      3,254      100.00

. 
. gen doubtstate = 8-truststate
(20 missing values generated)

. gen doubtmedia = 8-trustmedia
(23 missing values generated)

. gen doubtpress = 8-trustpress
(24 missing values generated)

. gen doubtsocmed = 8-trustsocmed
(41 missing values generated)

. 
. 
. *How truthfully has the government informed?
. tab inst3, mis

Wie wahrheitsgetreu hat Bundesreg. |
             über den Ausbruch des |
           Corona-Virus informiert |      Freq.     Percent        Cum.
-----------------------------------+-----------------------------------
1. überhaupt nicht wahrheitsgetreu |        349       10.65       10.65
          2. wenig wahrheitsgetreu |        690       21.05       31.70
                     3. weder noch |        702       21.42       53.11
       4. ziemlich wahrheitsgetreu |      1,252       38.19       91.31
           5. sehr wahrheitsgetreu |        258        7.87       99.18
                                 . |         27        0.82      100.00
-----------------------------------+-----------------------------------
                             Total |      3,278      100.00

. recast double inst3

. gen doubtgovtruth = 6-inst3
(27 missing values generated)

. tab doubtgovtruth, mis

doubtgovtru |
         th |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        258        7.87        7.87
          2 |      1,252       38.19       46.06
          3 |        702       21.42       67.48
          4 |        690       21.05       88.53
          5 |        349       10.65       99.18
          . |         27        0.82      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. 
. *General trust
. tab inst8, mis

       Vertrauen im Umgang mit Menschen |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
0. 0 - Man kann nicht vorsichtig genug  |        340       10.37       10.37
                                   1. 1 |        215        6.56       16.93
                                   2. 2 |        400       12.20       29.13
                                   3. 3 |        421       12.84       41.98
                                   4. 4 |        280        8.54       50.52
                                   5. 5 |        647       19.74       70.26
                                   6. 6 |        282        8.60       78.86
                                   7. 7 |        333       10.16       89.02
                                   8. 8 |        232        7.08       96.10
                                   9. 9 |         54        1.65       97.74
10. 10 - Man kann den Menschen vertraue |         41        1.25       98.99
                                      . |         33        1.01      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

. recast double inst8

. gen gentrust = inst8
(33 missing values generated)

. tab gentrust, mis

   gentrust |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        340       10.37       10.37
          1 |        215        6.56       16.93
          2 |        400       12.20       29.13
          3 |        421       12.84       41.98
          4 |        280        8.54       50.52
          5 |        647       19.74       70.26
          6 |        282        8.60       78.86
          7 |        333       10.16       89.02
          8 |        232        7.08       96.10
          9 |         54        1.65       97.74
         10 |         41        1.25       98.99
          . |         33        1.01      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. 
. *Communism cleavage, strong transfer state
. tab umv4, mis

  Wie viel Umverteilung der |
  Einkommen befürworten Sie |
    zwischen den Bürgern in |
                       Dtld |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
  0. 0 - keine Umverteilung |        407       12.42       12.42
                       1. 1 |        197        6.01       18.43
                       2. 2 |        347       10.59       29.01
                       3. 3 |        348       10.62       39.63
                       4. 4 |        261        7.96       47.59
                       5. 5 |        717       21.87       69.46
                       6. 6 |        305        9.30       78.77
                       7. 7 |        279        8.51       87.28
                       8. 8 |        185        5.64       92.92
                       9. 9 |         40        1.22       94.14
10. 10 - volle Umverteilung |        146        4.45       98.60
                          . |         46        1.40      100.00
----------------------------+-----------------------------------
                      Total |      3,278      100.00

. gen double protransfer = umv4
(46 missing values generated)

. tab protransfer, mis

protransfer |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        407       12.42       12.42
          1 |        197        6.01       18.43
          2 |        347       10.59       29.01
          3 |        348       10.62       39.63
          4 |        261        7.96       47.59
          5 |        717       21.87       69.46
          6 |        305        9.30       78.77
          7 |        279        8.51       87.28
          8 |        185        5.64       92.92
          9 |         40        1.22       94.14
         10 |        146        4.45       98.60
          . |         46        1.40      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Covid risk group
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. *Trust in health system if infected
. tab welf5, mis

       Vertrauen in |
  Gesundheitssystem |
   bei Corona, dass |
      man benötigte |
 Versorgung bekommt |      Freq.     Percent        Cum.
--------------------+-----------------------------------
   1. 1 - sehr hoch |        841       25.66       25.66
               2. 2 |      1,347       41.09       66.75
               3. 3 |        717       21.87       88.62
               4. 4 |        212        6.47       95.09
5. 5 - sehr niedrig |        141        4.30       99.39
                  . |         20        0.61      100.00
--------------------+-----------------------------------
              Total |      3,278      100.00

. recast double welf5

. gen trusthsyscorona = 6-welf5
(20 missing values generated)

. gen doubthsyscorona = welf5
(20 missing values generated)

. tab1 trusthsyscorona doubthsyscorona, mis

-> tabulation of trusthsyscorona  

trusthsysco |
       rona |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        141        4.30        4.30
          2 |        212        6.47       10.77
          3 |        717       21.87       32.64
          4 |      1,347       41.09       73.73
          5 |        841       25.66       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of doubthsyscorona  

doubthsysco |
       rona |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        841       25.66       25.66
          2 |      1,347       41.09       66.75
          3 |        717       21.87       88.62
          4 |        212        6.47       95.09
          5 |        141        4.30       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Risk group
. tab ges4 , mis //problem: many refusals

    Gehören Sie |
   aufgrund von |
Vorerkrankungen |
            zur |
Corona-Risikogr |
          uppe? |      Freq.     Percent        Cum.
----------------+-----------------------------------
        0. Nein |      1,932       58.94       58.94
          1. Ja |      1,192       36.36       95.30
9. Keine Angabe |        122        3.72       99.02
              . |         32        0.98      100.00
----------------+-----------------------------------
          Total |      3,278      100.00

. gen float riskgroup01 = ges4 if(ges4~=9 & ges4~=.)
(154 missing values generated)

. gen float riskgroup = ges4
(32 missing values generated)

. gen float riskgroupmiss = ges4==9 if ges4~=.
(32 missing values generated)

. tab1 riskgroup*, mis

-> tabulation of riskgroup01  

riskgroup01 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,932       58.94       58.94
          1 |      1,192       36.36       95.30
          . |        154        4.70      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of riskgroup  

  riskgroup |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,932       58.94       58.94
          1 |      1,192       36.36       95.30
          9 |        122        3.72       99.02
          . |         32        0.98      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of riskgroupmiss  

riskgroupmi |
         ss |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,124       95.30       95.30
          1 |        122        3.72       99.02
          . |         32        0.98      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *General health
. tab ges1, mis

       Wie würden Sie Ihren derzeitigen |
        Gesundheitszustand beschreiben? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Mir geht es gesundheitlich sehr gut. |        672       20.50       20.50
     2. Mir geht es gesundheitlich gut. |      1,055       32.18       52.68
3. Ich habe kleinere Beschwerden, mit d |        840       25.63       78.31
4. Ich habe gesundheitliche Probleme, d |        510       15.56       93.87
5. Ich leide an einer ernsthaften Krank |        130        3.97       97.83
           6. Möchte ich nicht angeben. |         51        1.56       99.39
                                      . |         20        0.61      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

. gen float health = 6-ges1 if ges1<6
(71 missing values generated)

. tab health, mis

     health |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        130        3.97        3.97
          2 |        510       15.56       19.52
          3 |        840       25.63       45.15
          4 |      1,055       32.18       77.33
          5 |        672       20.50       97.83
          . |         71        2.17      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. gen badhealth = 6-health
(71 missing values generated)

. tab badhealth

  badhealth |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        672       20.95       20.95
          2 |      1,055       32.90       53.85
          3 |        840       26.19       80.04
          4 |        510       15.90       95.95
          5 |        130        4.05      100.00
------------+-----------------------------------
      Total |      3,207      100.00

. 
. *Contact with infected people
. tab1 ges3*, mis

-> tabulation of ges3  

  Jemand im |
Bekanntenkr |
   eis, der |
positiv auf |
Corona-Viru |
 s getestet |      Freq.     Percent        Cum.
------------+-----------------------------------
    0. Nein |      2,661       81.18       81.18
      1. Ja |        587       17.91       99.08
          . |         30        0.92      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of ges3_other  

               Jemand im |
     Bekanntenkreis, der |
positiv auf Corona-Virus |
       getestet - Anzahl |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
                         |         20        0.61        0.61
                         |      2,671       81.48       82.09
                       0 |          1        0.03       82.12
                       1 |        265        8.08       90.21
                      10 |         11        0.34       90.54
                   10-20 |          1        0.03       90.57
                     100 |          1        0.03       90.60
                      17 |          1        0.03       90.63
                       2 |        174        5.31       95.94
                       3 |         71        2.17       98.11
                       4 |         26        0.79       98.90
                      40 |          1        0.03       98.93
                       5 |         17        0.52       99.45
                       6 |         10        0.31       99.76
                     6-8 |          1        0.03       99.79
                       7 |          1        0.03       99.82
                       8 |          1        0.03       99.85
                    Eine |          1        0.03       99.88
                 Familie |          1        0.03       99.91
                    Fünf |          1        0.03       99.94
In der Pflege arbeitende |          1        0.03       99.97
                  einige |          1        0.03      100.00
-------------------------+-----------------------------------
                   Total |      3,278      100.00

. gen float contact = ges3
(30 missing values generated)

. replace contact = 1 if (ges3_other~=" ")
(20 real changes made)

. tab contact, mis 

    contact |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,661       81.18       81.18
          1 |        607       18.52       99.69
          . |         10        0.31      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Sociodemographics and other
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Life satisfaction
. tab zufr1, mis

      Wie zufrieden sind Sie |
       gegenwärtig, alles in |
     allem, mit Ihrem Leben? |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
0. überhaupt nicht zufrieden |         25        0.76        0.76
                        1. 1 |         32        0.98        1.74
                        2. 2 |         63        1.92        3.66
                        3. 3 |        144        4.39        8.05
                        4. 4 |        126        3.84       11.90
                        5. 5 |        378       11.53       23.43
                        6. 6 |        320        9.76       33.19
                        7. 7 |        682       20.81       54.00
                        8. 8 |        822       25.08       79.07
                        9. 9 |        397       12.11       91.18
        10. völlig zufrieden |        264        8.05       99.24
                           . |         25        0.76      100.00
-----------------------------+-----------------------------------
                       Total |      3,278      100.00

. gen double lsatisfac = zufr1
(25 missing values generated)

. tab lsatisfac, mis

  lsatisfac |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         25        0.76        0.76
          1 |         32        0.98        1.74
          2 |         63        1.92        3.66
          3 |        144        4.39        8.05
          4 |        126        3.84       11.90
          5 |        378       11.53       23.43
          6 |        320        9.76       33.19
          7 |        682       20.81       54.00
          8 |        822       25.08       79.07
          9 |        397       12.11       91.18
         10 |        264        8.05       99.24
          . |         25        0.76      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Sex
. tab stat1, mis

Sind Sie... |      Freq.     Percent        Cum.
------------+-----------------------------------
0. Männlich |      1,594       48.63       48.63
1. Weiblich |      1,660       50.64       99.27
  2. Divers |          4        0.12       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. recast float stat1

. recode stat1 (0=0) (1 2=1), gen(female)
(4 differences between stat1 and female)

. label var female ""

. tab female, mis

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,594       48.63       48.63
          1 |      1,664       50.76       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Age
. tab1 stat2*, mis

-> tabulation of stat2  

 In welchem Jahr sind |
         Sie geboren? |      Freq.     Percent        Cum.
----------------------+-----------------------------------
1920. 1920 und früher |          1        0.03        0.03
           1929. 1929 |          1        0.03        0.06
           1930. 1930 |          1        0.03        0.09
           1931. 1931 |          1        0.03        0.12
           1933. 1933 |          3        0.09        0.21
           1935. 1935 |          4        0.12        0.34
           1936. 1936 |          2        0.06        0.40
           1937. 1937 |         11        0.34        0.73
           1938. 1938 |         14        0.43        1.16
           1939. 1939 |         15        0.46        1.62
           1940. 1940 |         27        0.82        2.44
           1941. 1941 |         30        0.92        3.36
           1942. 1942 |         24        0.73        4.09
           1943. 1943 |         30        0.92        5.00
           1944. 1944 |         24        0.73        5.74
           1945. 1945 |         19        0.58        6.31
           1946. 1946 |         29        0.88        7.20
           1947. 1947 |         50        1.53        8.72
           1948. 1948 |         55        1.68       10.40
           1949. 1949 |         78        2.38       12.78
           1950. 1950 |         63        1.92       14.70
           1951. 1951 |         64        1.95       16.66
           1952. 1952 |         73        2.23       18.88
           1953. 1953 |         65        1.98       20.87
           1954. 1954 |         88        2.68       23.55
           1955. 1955 |         67        2.04       25.59
           1956. 1956 |         50        1.53       27.12
           1957. 1957 |         60        1.83       28.95
           1958. 1958 |         44        1.34       30.29
           1959. 1959 |         61        1.86       32.15
           1960. 1960 |         58        1.77       33.92
           1961. 1961 |         54        1.65       35.57
           1962. 1962 |         46        1.40       36.97
           1963. 1963 |         57        1.74       38.71
           1964. 1964 |         55        1.68       40.39
           1965. 1965 |         77        2.35       42.74
           1966. 1966 |         64        1.95       44.69
           1967. 1967 |         73        2.23       46.92
           1968. 1968 |         52        1.59       48.51
           1969. 1969 |         54        1.65       50.15
           1970. 1970 |         66        2.01       52.17
           1971. 1971 |         58        1.77       53.94
           1972. 1972 |         45        1.37       55.31
           1973. 1973 |         39        1.19       56.50
           1974. 1974 |         50        1.53       58.02
           1975. 1975 |         54        1.65       59.67
           1976. 1976 |         47        1.43       61.10
           1977. 1977 |         67        2.04       63.15
           1978. 1978 |         62        1.89       65.04
           1979. 1979 |         55        1.68       66.72
           1980. 1980 |         65        1.98       68.70
           1981. 1981 |         63        1.92       70.62
           1982. 1982 |         59        1.80       72.42
           1983. 1983 |         74        2.26       74.68
           1984. 1984 |         62        1.89       76.57
           1985. 1985 |         76        2.32       78.89
           1986. 1986 |         64        1.95       80.84
           1987. 1987 |         53        1.62       82.46
           1988. 1988 |         72        2.20       84.66
           1989. 1989 |         53        1.62       86.27
           1990. 1990 |         46        1.40       87.68
           1991. 1991 |         67        2.04       89.72
           1992. 1992 |         66        2.01       91.73
           1993. 1993 |         65        1.98       93.72
           1994. 1994 |         58        1.77       95.49
           1995. 1995 |         47        1.43       96.92
           1996. 1996 |         20        0.61       97.53
           1997. 1997 |         19        0.58       98.11
           1998. 1998 |         18        0.55       98.66
           1999. 1999 |         13        0.40       99.05
           2000. 2000 |          4        0.12       99.18
           2001. 2001 |          3        0.09       99.27
           2002. 2002 |          4        0.12       99.39
                    . |         20        0.61      100.00
----------------------+-----------------------------------
                Total |      3,278      100.00

-> tabulation of stat2_gr3  

         Alter |
  gruppiert (3 |
      Gruppen) |      Freq.     Percent        Cum.
---------------+-----------------------------------
1. 18-39 Jahre |      1,006       30.69       30.69
2. 40-59 Jahre |      1,140       34.78       65.47
 3. 60 Jahre + |      1,112       33.92       99.39
             . |         20        0.61      100.00
---------------+-----------------------------------
         Total |      3,278      100.00

. gen double age = 2020-stat2
(20 missing values generated)

. tab age, mis

        age |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |          4        0.12        0.12
         19 |          3        0.09        0.21
         20 |          4        0.12        0.34
         21 |         13        0.40        0.73
         22 |         18        0.55        1.28
         23 |         19        0.58        1.86
         24 |         20        0.61        2.47
         25 |         47        1.43        3.90
         26 |         58        1.77        5.67
         27 |         65        1.98        7.66
         28 |         66        2.01        9.67
         29 |         67        2.04       11.71
         30 |         46        1.40       13.12
         31 |         53        1.62       14.73
         32 |         72        2.20       16.93
         33 |         53        1.62       18.55
         34 |         64        1.95       20.50
         35 |         76        2.32       22.82
         36 |         62        1.89       24.71
         37 |         74        2.26       26.97
         38 |         59        1.80       28.77
         39 |         63        1.92       30.69
         40 |         65        1.98       32.67
         41 |         55        1.68       34.35
         42 |         62        1.89       36.24
         43 |         67        2.04       38.29
         44 |         47        1.43       39.72
         45 |         54        1.65       41.37
         46 |         50        1.53       42.89
         47 |         39        1.19       44.08
         48 |         45        1.37       45.45
         49 |         58        1.77       47.22
         50 |         66        2.01       49.24
         51 |         54        1.65       50.88
         52 |         52        1.59       52.47
         53 |         73        2.23       54.70
         54 |         64        1.95       56.65
         55 |         77        2.35       59.00
         56 |         55        1.68       60.68
         57 |         57        1.74       62.42
         58 |         46        1.40       63.82
         59 |         54        1.65       65.47
         60 |         58        1.77       67.24
         61 |         61        1.86       69.10
         62 |         44        1.34       70.44
         63 |         60        1.83       72.27
         64 |         50        1.53       73.79
         65 |         67        2.04       75.84
         66 |         88        2.68       78.52
         67 |         65        1.98       80.51
         68 |         73        2.23       82.73
         69 |         64        1.95       84.69
         70 |         63        1.92       86.61
         71 |         78        2.38       88.99
         72 |         55        1.68       90.67
         73 |         50        1.53       92.19
         74 |         29        0.88       93.08
         75 |         19        0.58       93.65
         76 |         24        0.73       94.39
         77 |         30        0.92       95.30
         78 |         24        0.73       96.03
         79 |         30        0.92       96.95
         80 |         27        0.82       97.77
         81 |         15        0.46       98.23
         82 |         14        0.43       98.66
         83 |         11        0.34       98.99
         84 |          2        0.06       99.05
         85 |          4        0.12       99.18
         87 |          3        0.09       99.27
         89 |          1        0.03       99.30
         90 |          1        0.03       99.33
         91 |          1        0.03       99.36
        100 |          1        0.03       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. gen agedec = age/10
(20 missing values generated)

. 
. gen old = age>70

. tab old, mis

        old |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,839       86.61       86.61
          1 |        439       13.39      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Eastern Germany
. tab stat4 ost, mis //Berlin is grouped into Eastern, which we won't do

In welchem Bundesland |          Ostdeutschland
           leben Sie? | 0. Westde  1. Ostdeu          . |     Total
----------------------+---------------------------------+----------
 1. Baden-Württemberg |       327          0          0 |       327 
            2. Bayern |       387          0          0 |       387 
            3. Berlin |         0        292          0 |       292 
       4. Brandenburg |         0        196          0 |       196 
            5. Bremen |        28          0          0 |        28 
           6. Hamburg |        58          0          0 |        58 
            7. Hessen |       187          0          0 |       187 
8. Mecklenburg-Vorpom |         0        117          0 |       117 
     9. Niedersachsen |       222          0          0 |       222 
10. Nordrhein-Westfal |       523          0          0 |       523 
  11. Rheinland-Pfalz |       125          0          0 |       125 
         12. Saarland |        27          0          0 |        27 
          13. Sachsen |         0        327          0 |       327 
   14. Sachsen-Anhalt |         0        192          0 |       192 
15. Schleswig-Holstei |        82          0          0 |        82 
        16. Thüringen |         0        168          0 |       168 
                    . |         0          0         20 |        20 
----------------------+---------------------------------+----------
                Total |     1,966      1,292         20 |     3,278 

. gen float east=ost
(20 missing values generated)

. replace east=0 if stat4==3
(292 real changes made)

. tab stat4 east, mis

In welchem Bundesland |               east
           leben Sie? |         0          1          . |     Total
----------------------+---------------------------------+----------
 1. Baden-Württemberg |       327          0          0 |       327 
            2. Bayern |       387          0          0 |       387 
            3. Berlin |       292          0          0 |       292 
       4. Brandenburg |         0        196          0 |       196 
            5. Bremen |        28          0          0 |        28 
           6. Hamburg |        58          0          0 |        58 
            7. Hessen |       187          0          0 |       187 
8. Mecklenburg-Vorpom |         0        117          0 |       117 
     9. Niedersachsen |       222          0          0 |       222 
10. Nordrhein-Westfal |       523          0          0 |       523 
  11. Rheinland-Pfalz |       125          0          0 |       125 
         12. Saarland |        27          0          0 |        27 
          13. Sachsen |         0        327          0 |       327 
   14. Sachsen-Anhalt |         0        192          0 |       192 
15. Schleswig-Holstei |        82          0          0 |        82 
        16. Thüringen |         0        168          0 |       168 
                    . |         0          0         20 |        20 
----------------------+---------------------------------+----------
                Total |     2,258      1,000         20 |     3,278 

. tab east

       east |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,258       69.31       69.31
          1 |      1,000       30.69      100.00
------------+-----------------------------------
      Total |      3,258      100.00

. 
. tab stat7, mis

  In welchem Bundesland haben |
    Sie die meiste Zeit Ihrer |
          Kindheit verbracht? |      Freq.     Percent        Cum.
------------------------------+-----------------------------------
         1. Baden-Württemberg |        302        9.21        9.21
                    2. Bayern |        340       10.37       19.59
             3. Berlin (West) |        145        4.42       24.01
               4. Brandenburg |        172        5.25       29.26
                    5. Bremen |         25        0.76       30.02
                   6. Hamburg |         53        1.62       31.64
                    7. Hessen |        176        5.37       37.00
    8. Mecklenburg-Vorpommern |        116        3.54       40.54
             9. Niedersachsen |        203        6.19       46.74
      10. Nordrhein-Westfalen |        553       16.87       63.61
          11. Rheinland-Pfalz |        131        4.00       67.60
                 12. Saarland |         37        1.13       68.73
                  13. Sachsen |        337       10.28       79.01
           14. Sachsen-Anhalt |        215        6.56       85.57
       15. Schleswig-Holstein |         67        2.04       87.61
                16. Thüringen |        196        5.98       93.59
17. Außerhalb von Deutschland |         69        2.10       95.70
             18. Berlin (Ost) |        104        3.17       98.87
                            . |         37        1.13      100.00
------------------------------+-----------------------------------
                        Total |      3,278      100.00

. recast float stat7

. recode stat7 (1/3 5/7 9/12 15 17=0) (4 8 13 14 16 18 = 1) (.=.), gen(eastchild)
(3241 differences between stat7 and eastchild)

. label var eastchild ""

. tab stat7 eastchild, mis

In welchem Bundesland |
 haben Sie die meiste |
  Zeit Ihrer Kindheit |            eastchild
           verbracht? |         0          1          . |     Total
----------------------+---------------------------------+----------
 1. Baden-Württemberg |       302          0          0 |       302 
            2. Bayern |       340          0          0 |       340 
     3. Berlin (West) |       145          0          0 |       145 
       4. Brandenburg |         0        172          0 |       172 
            5. Bremen |        25          0          0 |        25 
           6. Hamburg |        53          0          0 |        53 
            7. Hessen |       176          0          0 |       176 
8. Mecklenburg-Vorpom |         0        116          0 |       116 
     9. Niedersachsen |       203          0          0 |       203 
10. Nordrhein-Westfal |       553          0          0 |       553 
  11. Rheinland-Pfalz |       131          0          0 |       131 
         12. Saarland |        37          0          0 |        37 
          13. Sachsen |         0        337          0 |       337 
   14. Sachsen-Anhalt |         0        215          0 |       215 
15. Schleswig-Holstei |        67          0          0 |        67 
        16. Thüringen |         0        196          0 |       196 
17. Außerhalb von Deu |        69          0          0 |        69 
     18. Berlin (Ost) |         0        104          0 |       104 
                    . |         0          0         37 |        37 
----------------------+---------------------------------+----------
                Total |     2,101      1,140         37 |     3,278 

. 
. *City vs. countryside
. tab stat5, mis

   Was trifft am ehesten auf die Gegend |
      zu, in der Sie leben?  Ist das... |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                      1. eine Großstadt |      1,033       31.51       31.51
2. ein Vorort oder Randgebiet einer Gro |        397       12.11       43.62
          3. eine Stadt oder Kleinstadt |      1,111       33.89       77.52
    4. ein Dorf oder ländliche Umgebung |        703       21.45       98.96
                                      . |         34        1.04      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

. gen float habitation = stat5
(34 missing values generated)

. label value habitation stat5

. tab habitation, mis

                             habitation |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                      1. eine Großstadt |      1,033       31.51       31.51
2. ein Vorort oder Randgebiet einer Gro |        397       12.11       43.62
          3. eine Stadt oder Kleinstadt |      1,111       33.89       77.52
    4. ein Dorf oder ländliche Umgebung |        703       21.45       98.96
                                      . |         34        1.04      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

. 
. *Migration background
. tab1 stat3*, mis nol

-> tabulation of stat3a  

[Ich bin in |
Deutschland |
  geboren.] |
 Was trifft |
auf Sie zu? |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        154        4.70        4.70
          1 |      3,099       94.54       99.24
          8 |          5        0.15       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of stat3b  

     [Meine |
 Mutter ist |
         in |
Deutschland |
  geboren.] |
 Was trifft |
auf Sie zu? |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        304        9.27        9.27
          1 |      2,936       89.57       98.84
          8 |         18        0.55       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

-> tabulation of stat3c  

[Mein Vater |
     ist in |
Deutschland |
  geboren.] |
 Was trifft |
auf Sie zu? |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        323        9.85        9.85
          1 |      2,907       88.68       98.54
          8 |         28        0.85       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. gen mig = 0 if (stat3a~=. & stat3b~=. & stat3c~=.)
(20 missing values generated)

. replace mig = 1 if (stat3a==0 | stat3b==0 | stat3c==0)
(417 real changes made)

. replace mig=. if (stat3a==8 | stat3b==8 | stat3c==8)
(34 real changes made, 34 to missing)

. tab mig, mis

        mig |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,812       85.78       85.78
          1 |        412       12.57       98.35
          . |         54        1.65      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Children
. tab stat12, mis

  Haben Sie |
    Kinder? |      Freq.     Percent        Cum.
------------+-----------------------------------
    0. Nein |      1,237       37.74       37.74
      1. Ja |      1,999       60.98       98.72
          . |         42        1.28      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. gen float children = stat12
(42 missing values generated)

. tab children, mis

   children |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,237       37.74       37.74
          1 |      1,999       60.98       98.72
          . |         42        1.28      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Children at home
. tab1 stat14 stat13, mis

-> tabulation of stat14  

 Und wie viele |
    davon sind |
  Kinder unter |
    16 Jahren? |      Freq.     Percent        Cum.
---------------+-----------------------------------
     0. keines |      1,741       53.11       53.11
          1. 1 |        345       10.52       63.64
          2. 2 |        236        7.20       70.84
          3. 3 |         52        1.59       72.42
          4. 4 |         14        0.43       72.85
5. 5 oder mehr |          7        0.21       73.06
             . |         52        1.59       74.65
            .f |        831       25.35      100.00
---------------+-----------------------------------
         Total |      3,278      100.00

-> tabulation of stat13  

     Wie viele |
Personen leben |
    derzeit in |
         Ihrem |
 Haushalt, Sie |
        selbst |
eingeschlossen |
             ? |      Freq.     Percent        Cum.
---------------+-----------------------------------
          1. 1 |        831       25.35       25.35
          2. 2 |      1,490       45.45       70.81
          3. 3 |        491       14.98       85.78
          4. 4 |        322        9.82       95.61
          5. 5 |         85        2.59       98.20
6. 6 oder mehr |         39        1.19       99.39
             . |         20        0.61      100.00
---------------+-----------------------------------
         Total |      3,278      100.00

. gen float childrenathome = stat14>0 if stat14~=.
(52 missing values generated)

. replace childrenathome = 0 if stat13==1
(831 real changes made)

. tab childrenathome, mis

childrenath |
        ome |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,572       78.46       78.46
          1 |        654       19.95       98.41
          . |         52        1.59      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *General education in years (nur only general education)
. tab1 stat16*, mis

-> tabulation of stat16  

    Welchen höchsten allgemeinbildenden |
              Schulabschluss haben Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
             1. Noch in Schulausbildung |         12        0.37        0.37
2. Von der Schule abgegangen ohne Schul |         57        1.74        2.10
3. Haupt- oder Volksschulabschluss bzw. |        961       29.32       31.42
4. Mittlere Reife, Realschulabschluss b |      1,143       34.87       66.29
5. Fachhochschulreife (Abschluss einer  |        284        8.66       74.95
6. Abitur bzw. Erweiterte Oberschule mi |        801       24.44       99.39
                                      . |         20        0.61      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of stat16_gr3  

    Bildung gruppiert |      Freq.     Percent        Cum.
----------------------+-----------------------------------
1. Niedrigere Bildung |      1,030       31.42       31.42
  2. Mittlere Bildung |      1,143       34.87       66.29
    3. Höhere Bildung |      1,085       33.10       99.39
                    . |         20        0.61      100.00
----------------------+-----------------------------------
                Total |      3,278      100.00

. recast float stat16

. recode stat16 (1=.) (2=7) (3=9) (4=10) (5=12) (6=13), gen(educgenyr)
(3258 differences between stat16 and educgenyr)

. tab1 stat17* educgenyr, mis

-> tabulation of stat17  

           Welchen höchsten beruflichen |
        Ausbildungsabschluss haben Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Beruflich-betriebliche Anlernzeit mi |         64        1.95        1.95
           2. Teilfacharbeiterabschluss |         21        0.64        2.59
3. Abgeschlossene gewerbliche oder land |        533       16.26       18.85
  4. Abgeschlossene kaufmännische Lehre |        860       26.24       45.09
  5. Berufliches Praktikum, Volontariat |         10        0.31       45.39
            6. Berufsfachschulabschluss |        368       11.23       56.62
                  7. Fachschulabschluss |        147        4.48       61.10
8. Meister-, Techniker- oder gleichwert |        189        5.77       66.87
9. Fachhochschulabschluss (auch Abschlu |        203        6.19       73.06
                 10. Hochschulabschluss |        438       13.36       86.42
11. Keinen beruflichen Ausbildungsabsch |        249        7.60       94.02
                          12. Sonstiges |        146        4.45       98.47
                                      . |         38        1.16       99.63
                                     .f |         12        0.37      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of stat17_other  

           [Sonstiges] Welchen höchsten |
 beruflichen Ausbildungsabschluss haben |
                                   Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                        |         20        0.61        0.61
                                        |         30        0.92        1.53
                                      . |          2        0.06        1.59
                                .FILTER |      3,082       94.02       95.61
        Abgeschl. technische Ausbildung |          1        0.03       95.64
              Abgeschlossene Ausbildung |          1        0.03       95.67
      Abgeschlossene Facharbeiter Lehre |          1        0.03       95.70
   Abgeschlossene Gesundheitsausbildung |          1        0.03       95.73
                   Abgeschlossene Lehre |          1        0.03       95.76
Abgeschlossene Lehre freie Gerichtsba.. |          1        0.03       95.79
            Abgeschlossene techn. Lehre |          1        0.03       95.82
                            Altenpflege |          1        0.03       95.85
        Angestellte öffentlicher Dienst |          1        0.03       95.88
                    Anwärterschaft Zoll |          1        0.03       95.91
                           Arzthelferin |          1        0.03       95.94
                  Augenoptik 2.Lehrjahr |          1        0.03       95.97
               Ausbildung im IT Bereich |          1        0.03       96.00
                   Ausbildung mi Diplom |          1        0.03       96.03
Ausbildung zum Offizier, höherer Dienst |          1        0.03       96.06
                               Bachelor |          1        0.03       96.10
                            Bauzeichner |          1        0.03       96.13
Beamtenausbildung mit Fachhochschulre.. |          1        0.03       96.16
                                Beamter |          2        0.06       96.22
                          Beamter, Bund |          1        0.03       96.25
                                Beamtin |          2        0.06       96.31
              Berufsausbildung zum Koch |          1        0.03       96.34
                    Berufschulabschluss |          1        0.03       96.37
              Berufskraftfahrer  maurer |          1        0.03       96.40
                    Berufskraftfarerinn |          1        0.03       96.43
   Betriebsingenieur mit Eintragung NRW |          1        0.03       96.46
                      Betriebsschlosser |          1        0.03       96.49
                           Betriebswirt |          1        0.03       96.52
                         Betriebswirtin |          1        0.03       96.55
                         Binnenschiffer |          1        0.03       96.58
                           Buchbinderin |          1        0.03       96.61
               Bäcker und Konditor Koch |          1        0.03       96.64
                              Chemikant |          1        0.03       96.67
                             Dachdecker |          1        0.03       96.71
                             Erzieherin |          2        0.06       96.77
                  Exam Krankenschwester |          1        0.03       96.80
        Examen im medizinischen Bereich |          1        0.03       96.83
            Examinierte Pflegefachkraft |          1        0.03       96.86
                           Facharbeiter |          7        0.21       97.07
            Facharbeiter für Nahverkehr |          1        0.03       97.10
                  Facharbeiterabschluss |          1        0.03       97.13
                   Facharbeiterabschluß |          2        0.06       97.19
                  Facharbeitwrabschluss |          1        0.03       97.22
               Fachfrau für Gastronomie |          1        0.03       97.25
  Fachkraft für Brief und Frachtverkehr |          1        0.03       97.28
    Fachkraft für Schutz und sicherheit |          1        0.03       97.32
               Fachkraft im Gastgewerbe |          1        0.03       97.35
                             Fahrlehrer |          1        0.03       97.38
                              Fkeischer |          1        0.03       97.41
                                Friseur |          1        0.03       97.44
                           Friseurlehre |          1        0.03       97.47
     Frisörin+examinierte Altenpfegerin |          1        0.03       97.50
                            Gastronomie |          1        0.03       97.53
                        Gebädereinigung |          1        0.03       97.56
                          Gesellenbrief |          2        0.06       97.62
      Gesundheits-,und Krankenpflegerin |          1        0.03       97.65
                       Gesundheitswesen |          1        0.03       97.68
               Glas und Gebäudereiniger |          1        0.03       97.71
                             Gleisbauer |          1        0.03       97.74
                      Goldschmiedelehre |          1        0.03       97.77
                               Handwerk |          1        0.03       97.80
                         Hauswirtschaft |          1        0.03       97.83
                     Hauswirtschafterin |          1        0.03       97.86
                         Hebammenexamen |          1        0.03       97.90
            Heilerziehungspflegerhelfer |          1        0.03       97.93
                          Hotelfachfrau |          1        0.03       97.96
                    Industriemechaniker |          1        0.03       97.99
Industriemechanikerin maschinen syste.. |          1        0.03       98.02
                        Jägerleitlizenz |          1        0.03       98.05
Kaufmännische und gewerbliche Ausbild.. |          1        0.03       98.08
                 Kinderkrankenschwester |          1        0.03       98.11
                                   Koch |          2        0.06       98.17
                          Krankenpflege |          1        0.03       98.20
                   Krankenplegehelferin |          1        0.03       98.23
                       Krankenschwester |          2        0.06       98.29
                     Laufbahnausbildung |          1        0.03       98.32
                                  Lehre |          2        0.06       98.38
 Lehre durch Flucht nicht abgeschlossen |          1        0.03       98.41
                                    MBA |          1        0.03       98.44
                                    MTA |          1        0.03       98.47
                          Maler Geselle |          1        0.03       98.51
            Maler und Lackierer Geselle |          1        0.03       98.54
      Maler und Lackierer mit Abschluss |          1        0.03       98.57
                                 Maurer |          1        0.03       98.60
                                Medizin |          1        0.03       98.63
           Medizinische Fachangestellte |          1        0.03       98.66
                                    Mfa |          1        0.03       98.69
                           Papiermacher |          1        0.03       98.72
           Personalfachkauffrau IHK AdA |          1        0.03       98.75
                                 Pflege |          1        0.03       98.78
            Pharmazeutische Assistentin |          1        0.03       98.81
                 Polizeivollzugsbeamter |          1        0.03       98.84
                            Postbeamtin |          1        0.03       98.87
                            Schneiderin |          1        0.03       98.90
                      Sozialassistenrin |          1        0.03       98.93
                 Staatliche Anerkennung |          1        0.03       98.96
           Staatluch anerkanntes Examen |          1        0.03       98.99
                           Staatsexamen |          2        0.06       99.05
                       Stenokontoristin |          1        0.03       99.08
                          Steuerberater |          1        0.03       99.12
                  Technische Zeichnerin |          1        0.03       99.15
                      Technischen Beruf |          1        0.03       99.18
                               Tischler |          2        0.06       99.24
                    Tourismusfachwirtin |          1        0.03       99.27
                            Verkäuferin |          1        0.03       99.30
                             Verwaltung |          1        0.03       99.33
            Verwaltungfachhsangestellte |          1        0.03       99.36
                Verwaltungsangestellter |          1        0.03       99.39
             Verwaltungsfachangestellte |          1        0.03       99.42
                      Verwaltungswirtin |          1        0.03       99.45
                            Volontariat |          1        0.03       99.48
                   Waldorfkindergärtner |          1        0.03       99.51
Werde einen Gesundheitskombikurs Pfle.. |          1        0.03       99.54
                       Zahnarzthelferin |          2        0.06       99.60
                Zahnmed.Fachangestellte |          1        0.03       99.63
  abgeschlossene Lehre Tierarzthelferin |          1        0.03       99.66
 abgeschlossene Lehre in der Verwaltung |          1        0.03       99.69
                                     db |          1        0.03       99.73
 gehobener Abschluss im öffentl. Dienst |          1        0.03       99.76
                      geprüfter Beamter |          1        0.03       99.79
                                kapitän |          1        0.03       99.82
                                 keinen |          1        0.03       99.85
                                  maler |          1        0.03       99.88
                medizinische Ausbildung |          1        0.03       99.91
                                rentner |          1        0.03       99.94
                            universität |          1        0.03       99.97
                    öffentlicher Dienst |          1        0.03      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of stat17p  

           Welchen höchsten beruflichen |
           Ausbildungsabschluss hat Ihr |
                 Partner/Ihre Partnerin |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Beruflich-betriebliche Anlernzeit mi |         43        1.31        1.31
           2. Teilfacharbeiterabschluss |         25        0.76        2.07
3. Abgeschlossene gewerbliche oder land |        420       12.81       14.89
  4. Abgeschlossene kaufmännische Lehre |        396       12.08       26.97
  5. Berufliches Praktikum, Volontariat |          7        0.21       27.18
            6. Berufsfachschulabschluss |        266        8.11       35.30
                  7. Fachschulabschluss |        151        4.61       39.90
8. Meister-, Techniker- oder gleichwert |        185        5.64       45.55
9. Fachhochschulabschluss (auch Abschlu |        130        3.97       49.51
                 10. Hochschulabschluss |        282        8.60       58.11
11. Keinen beruflichen Ausbildungsabsch |        126        3.84       61.96
                          12. Sonstiges |         83        2.53       64.49
                                      . |        286        8.72       73.22
                                     .f |        878       26.78      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of stat17p_other  

           [Sonstiges] Welchen höchsten |
   beruflichen Ausbildungsabschluss hat |
                         Ihr Partner/Ih |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                        |         20        0.61        0.61
                                        |      1,144       34.90       35.51
                                .FILTER |      2,031       61.96       97.47
      Abgeschlossene Facharbeiter Lehre |          1        0.03       97.50
                   Abgeschlossene Lehre |          1        0.03       97.53
 Abschluss im gehobenen öffentl. Dienst |          1        0.03       97.56
                            Angestellte |          1        0.03       97.59
                                Beamter |          5        0.15       97.74
                                Beamtin |          1        0.03       97.77
                      Berufskraftfahrer |          1        0.03       97.80
                           Betriebswirt |          1        0.03       97.83
                             Buchhalter |          1        0.03       97.86
                                 Bäcker |          1        0.03       97.90
                             Dachdecker |          1        0.03       97.93
                          Dipl Kauffrau |          1        0.03       97.96
                    Diplom Informatiker |          1        0.03       97.99
                      Diplom Schauspiel |          1        0.03       98.02
                                 Doktor |          1        0.03       98.05
                             Erzieherin |          1        0.03       98.08
                                 Examen |          1        0.03       98.11
                   Examen Krankenpflege |          1        0.03       98.14
                          Examen Pflege |          1        0.03       98.17
                  Facharbeiterabschluss |          1        0.03       98.20
                              Feinoptik |          1        0.03       98.23
                              Friseurin |          1        0.03       98.26
                          GESELLENBRIEF |          1        0.03       98.29
                          Gesellenbrief |          1        0.03       98.32
                             Gesundheit |          1        0.03       98.35
                       Gesundheitswesen |          1        0.03       98.38
                          Handelsschule |          1        0.03       98.41
                             Handwerker |          1        0.03       98.44
                          Heizungsbauer |          1        0.03       98.47
                                 Helfer |          1        0.03       98.51
                          Hotelfachfrau |          1        0.03       98.54
                              Industrie |          1        0.03       98.57
                                 Justiz |          1        0.03       98.60
                                Kellner |          1        0.03       98.63
                           KfZ Mechanik |          1        0.03       98.66
                                   Koch |          1        0.03       98.69
                Koch und Fitnesstrainer |          1        0.03       98.72
                            Kraftfahrer |          2        0.06       98.78
                       Krankenpflegerin |          1        0.03       98.81
                       Krankenschwester |          2        0.06       98.87
                                 Köchin |          1        0.03       98.90
                                  Lehre |          2        0.06       98.96
                              Lizentiat |          1        0.03       98.99
                              Lokführer |          1        0.03       99.02
                                  Maler |          1        0.03       99.05
                    Maler und Lackierer |          1        0.03       99.08
                                 Master |          2        0.06       99.15
                                 Maurer |          1        0.03       99.18
                                Meister |          1        0.03       99.21
             Metzger , gartenlandschaft |          1        0.03       99.24
                     Noch in Ausbildung |          2        0.06       99.30
                                  Re No |          1        0.03       99.33
      Realschulabschluss und Ausbildung |          1        0.03       99.36
                               Schloßer |          1        0.03       99.39
                       Stahl-Betonbauer |          1        0.03       99.42
                       Stenokontoristin |          1        0.03       99.45
                        Universalfräser |          1        0.03       99.48
                                  Weber |          1        0.03       99.51
                         Werkzeugmacher |          1        0.03       99.54
             Zahmedizin Fachangestellte |          1        0.03       99.57
                       Zahnarzthelferin |          1        0.03       99.60
                            Zollbeamtin |          1        0.03       99.63
        abgeschlossene technische Lehre |          1        0.03       99.66
                           arzthelferin |          1        0.03       99.69
                                beamtin |          1        0.03       99.73
                               berentet |          1        0.03       99.76
             betriebswirt des Handwerks |          1        0.03       99.79
                   exam. Altenpflegerin |          1        0.03       99.82
                       geprüfte Beamtin |          1        0.03       99.85
                                   koch |          1        0.03       99.88
                                 privat |          1        0.03       99.91
            staatl.anerkannte Podologin |          1        0.03       99.94
                            universität |          1        0.03       99.97
                        zahnarthelferin |          1        0.03      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of educgenyr  

  RECODE of |
     stat16 |
   (Welchen |
   höchsten |
allgemeinbi |
    ldenden |
Schulabschl |
  uss haben |
      Sie?) |      Freq.     Percent        Cum.
------------+-----------------------------------
          7 |         57        1.74        1.74
          9 |        961       29.32       31.06
         10 |      1,143       34.87       65.92
         12 |        284        8.66       74.59
         13 |        801       24.44       99.02
          . |         32        0.98      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *General education categorical
. gen float educgencat = stat16_gr3 
(20 missing values generated)

. tab educgencat, mis

 educgencat |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,030       31.42       31.42
          2 |      1,143       34.87       66.29
          3 |      1,085       33.10       99.39
          . |         20        0.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. tab educgencat stat16, mis

           |        Welchen höchsten allgemeinbildenden Schulabschluss haben Sie?
educgencat | 1. Noch i  2. Von de  3. Haupt-  4. Mittle  5. Fachho  6. Abitur          . |     Total
-----------+-----------------------------------------------------------------------------+----------
         1 |        12         57        961          0          0          0          0 |     1,030 
         2 |         0          0          0      1,143          0          0          0 |     1,143 
         3 |         0          0          0          0        284        801          0 |     1,085 
         . |         0          0          0          0          0          0         20 |        20 
-----------+-----------------------------------------------------------------------------+----------
     Total |        12         57        961      1,143        284        801         20 |     3,278 

. 
. *Vocational education in years
. tab1 stat17 stat17_other, mis

-> tabulation of stat17  

           Welchen höchsten beruflichen |
        Ausbildungsabschluss haben Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Beruflich-betriebliche Anlernzeit mi |         64        1.95        1.95
           2. Teilfacharbeiterabschluss |         21        0.64        2.59
3. Abgeschlossene gewerbliche oder land |        533       16.26       18.85
  4. Abgeschlossene kaufmännische Lehre |        860       26.24       45.09
  5. Berufliches Praktikum, Volontariat |         10        0.31       45.39
            6. Berufsfachschulabschluss |        368       11.23       56.62
                  7. Fachschulabschluss |        147        4.48       61.10
8. Meister-, Techniker- oder gleichwert |        189        5.77       66.87
9. Fachhochschulabschluss (auch Abschlu |        203        6.19       73.06
                 10. Hochschulabschluss |        438       13.36       86.42
11. Keinen beruflichen Ausbildungsabsch |        249        7.60       94.02
                          12. Sonstiges |        146        4.45       98.47
                                      . |         38        1.16       99.63
                                     .f |         12        0.37      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of stat17_other  

           [Sonstiges] Welchen höchsten |
 beruflichen Ausbildungsabschluss haben |
                                   Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                        |         20        0.61        0.61
                                        |         30        0.92        1.53
                                      . |          2        0.06        1.59
                                .FILTER |      3,082       94.02       95.61
        Abgeschl. technische Ausbildung |          1        0.03       95.64
              Abgeschlossene Ausbildung |          1        0.03       95.67
      Abgeschlossene Facharbeiter Lehre |          1        0.03       95.70
   Abgeschlossene Gesundheitsausbildung |          1        0.03       95.73
                   Abgeschlossene Lehre |          1        0.03       95.76
Abgeschlossene Lehre freie Gerichtsba.. |          1        0.03       95.79
            Abgeschlossene techn. Lehre |          1        0.03       95.82
                            Altenpflege |          1        0.03       95.85
        Angestellte öffentlicher Dienst |          1        0.03       95.88
                    Anwärterschaft Zoll |          1        0.03       95.91
                           Arzthelferin |          1        0.03       95.94
                  Augenoptik 2.Lehrjahr |          1        0.03       95.97
               Ausbildung im IT Bereich |          1        0.03       96.00
                   Ausbildung mi Diplom |          1        0.03       96.03
Ausbildung zum Offizier, höherer Dienst |          1        0.03       96.06
                               Bachelor |          1        0.03       96.10
                            Bauzeichner |          1        0.03       96.13
Beamtenausbildung mit Fachhochschulre.. |          1        0.03       96.16
                                Beamter |          2        0.06       96.22
                          Beamter, Bund |          1        0.03       96.25
                                Beamtin |          2        0.06       96.31
              Berufsausbildung zum Koch |          1        0.03       96.34
                    Berufschulabschluss |          1        0.03       96.37
              Berufskraftfahrer  maurer |          1        0.03       96.40
                    Berufskraftfarerinn |          1        0.03       96.43
   Betriebsingenieur mit Eintragung NRW |          1        0.03       96.46
                      Betriebsschlosser |          1        0.03       96.49
                           Betriebswirt |          1        0.03       96.52
                         Betriebswirtin |          1        0.03       96.55
                         Binnenschiffer |          1        0.03       96.58
                           Buchbinderin |          1        0.03       96.61
               Bäcker und Konditor Koch |          1        0.03       96.64
                              Chemikant |          1        0.03       96.67
                             Dachdecker |          1        0.03       96.71
                             Erzieherin |          2        0.06       96.77
                  Exam Krankenschwester |          1        0.03       96.80
        Examen im medizinischen Bereich |          1        0.03       96.83
            Examinierte Pflegefachkraft |          1        0.03       96.86
                           Facharbeiter |          7        0.21       97.07
            Facharbeiter für Nahverkehr |          1        0.03       97.10
                  Facharbeiterabschluss |          1        0.03       97.13
                   Facharbeiterabschluß |          2        0.06       97.19
                  Facharbeitwrabschluss |          1        0.03       97.22
               Fachfrau für Gastronomie |          1        0.03       97.25
  Fachkraft für Brief und Frachtverkehr |          1        0.03       97.28
    Fachkraft für Schutz und sicherheit |          1        0.03       97.32
               Fachkraft im Gastgewerbe |          1        0.03       97.35
                             Fahrlehrer |          1        0.03       97.38
                              Fkeischer |          1        0.03       97.41
                                Friseur |          1        0.03       97.44
                           Friseurlehre |          1        0.03       97.47
     Frisörin+examinierte Altenpfegerin |          1        0.03       97.50
                            Gastronomie |          1        0.03       97.53
                        Gebädereinigung |          1        0.03       97.56
                          Gesellenbrief |          2        0.06       97.62
      Gesundheits-,und Krankenpflegerin |          1        0.03       97.65
                       Gesundheitswesen |          1        0.03       97.68
               Glas und Gebäudereiniger |          1        0.03       97.71
                             Gleisbauer |          1        0.03       97.74
                      Goldschmiedelehre |          1        0.03       97.77
                               Handwerk |          1        0.03       97.80
                         Hauswirtschaft |          1        0.03       97.83
                     Hauswirtschafterin |          1        0.03       97.86
                         Hebammenexamen |          1        0.03       97.90
            Heilerziehungspflegerhelfer |          1        0.03       97.93
                          Hotelfachfrau |          1        0.03       97.96
                    Industriemechaniker |          1        0.03       97.99
Industriemechanikerin maschinen syste.. |          1        0.03       98.02
                        Jägerleitlizenz |          1        0.03       98.05
Kaufmännische und gewerbliche Ausbild.. |          1        0.03       98.08
                 Kinderkrankenschwester |          1        0.03       98.11
                                   Koch |          2        0.06       98.17
                          Krankenpflege |          1        0.03       98.20
                   Krankenplegehelferin |          1        0.03       98.23
                       Krankenschwester |          2        0.06       98.29
                     Laufbahnausbildung |          1        0.03       98.32
                                  Lehre |          2        0.06       98.38
 Lehre durch Flucht nicht abgeschlossen |          1        0.03       98.41
                                    MBA |          1        0.03       98.44
                                    MTA |          1        0.03       98.47
                          Maler Geselle |          1        0.03       98.51
            Maler und Lackierer Geselle |          1        0.03       98.54
      Maler und Lackierer mit Abschluss |          1        0.03       98.57
                                 Maurer |          1        0.03       98.60
                                Medizin |          1        0.03       98.63
           Medizinische Fachangestellte |          1        0.03       98.66
                                    Mfa |          1        0.03       98.69
                           Papiermacher |          1        0.03       98.72
           Personalfachkauffrau IHK AdA |          1        0.03       98.75
                                 Pflege |          1        0.03       98.78
            Pharmazeutische Assistentin |          1        0.03       98.81
                 Polizeivollzugsbeamter |          1        0.03       98.84
                            Postbeamtin |          1        0.03       98.87
                            Schneiderin |          1        0.03       98.90
                      Sozialassistenrin |          1        0.03       98.93
                 Staatliche Anerkennung |          1        0.03       98.96
           Staatluch anerkanntes Examen |          1        0.03       98.99
                           Staatsexamen |          2        0.06       99.05
                       Stenokontoristin |          1        0.03       99.08
                          Steuerberater |          1        0.03       99.12
                  Technische Zeichnerin |          1        0.03       99.15
                      Technischen Beruf |          1        0.03       99.18
                               Tischler |          2        0.06       99.24
                    Tourismusfachwirtin |          1        0.03       99.27
                            Verkäuferin |          1        0.03       99.30
                             Verwaltung |          1        0.03       99.33
            Verwaltungfachhsangestellte |          1        0.03       99.36
                Verwaltungsangestellter |          1        0.03       99.39
             Verwaltungsfachangestellte |          1        0.03       99.42
                      Verwaltungswirtin |          1        0.03       99.45
                            Volontariat |          1        0.03       99.48
                   Waldorfkindergärtner |          1        0.03       99.51
Werde einen Gesundheitskombikurs Pfle.. |          1        0.03       99.54
                       Zahnarzthelferin |          2        0.06       99.60
                Zahnmed.Fachangestellte |          1        0.03       99.63
  abgeschlossene Lehre Tierarzthelferin |          1        0.03       99.66
 abgeschlossene Lehre in der Verwaltung |          1        0.03       99.69
                                     db |          1        0.03       99.73
 gehobener Abschluss im öffentl. Dienst |          1        0.03       99.76
                      geprüfter Beamter |          1        0.03       99.79
                                kapitän |          1        0.03       99.82
                                 keinen |          1        0.03       99.85
                                  maler |          1        0.03       99.88
                medizinische Ausbildung |          1        0.03       99.91
                                rentner |          1        0.03       99.94
                            universität |          1        0.03       99.97
                    öffentlicher Dienst |          1        0.03      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

. recast float stat17

. recode stat17 (1=1) (2=1) (3=3) (4=3) (5=0.5) (6=3) (7=3) (8=4) (9=4) (10=3) (11=0) (12=2), gen(educvocyr)
(2631 differences between stat17 and educvocyr)

. tab educvocyr , mis

  RECODE of |
     stat17 |
   (Welchen |
   höchsten |
beruflichen |
Ausbildungs |
  abschluss |
haben Sie?) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        249        7.60        7.60
         .5 |         10        0.31        7.90
          1 |         85        2.59       10.49
          2 |        146        4.45       14.95
          3 |      2,346       71.57       86.52
          4 |        392       11.96       98.47
          . |         38        1.16       99.63
         .f |         12        0.37      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. tab stat18, mis

                   Um welche Art von |
(Fach-)Hochschulabschluss handelt es |
           sich dabei? Bitte geben S |      Freq.     Percent        Cum.
-------------------------------------+-----------------------------------
                         1. Bachelor |        123        3.75        3.75
                           2. Master |         75        2.29        6.04
                           3. Diplom |        282        8.60       14.64
                         4. Magister |         13        0.40       15.04
5. Staatsexamen oder Lehramtsprüfung |         75        2.29       17.33
                        6. Promotion |         40        1.22       18.55
              7. Sonstiger Abschluss |         32        0.98       19.52
                                   . |        446       13.61       33.13
                                  .f |      2,192       66.87      100.00
-------------------------------------+-----------------------------------
                               Total |      3,278      100.00

. recast float stat18

. recode stat18 (1=0) (2/5=2) (6=6) (7=1), gen(univyr)
(525 differences between stat18 and univyr)

. tab stat17 univyr, mis

     Welchen höchsten |
          beruflichen |   RECODE of stat18 (Um welche Art von (Fach-)Hochschulabschluss
 Ausbildungsabschluss |                        handelt es sich da
           haben Sie? |         0          1          2          6          .         .f |     Total
----------------------+------------------------------------------------------------------+----------
1. Beruflich-betriebl |         0          0          0          0          0         64 |        64 
2. Teilfacharbeiterab |         0          0          0          0          0         21 |        21 
3. Abgeschlossene gew |         0          0          0          0          0        533 |       533 
4. Abgeschlossene kau |         0          0          0          0          0        860 |       860 
5. Berufliches Prakti |         0          0          0          0          0         10 |        10 
6. Berufsfachschulabs |         0          0          0          0          0        368 |       368 
7. Fachschulabschluss |         0          0          0          0          0        147 |       147 
8. Meister-, Technike |         0          0          0          0          0        189 |       189 
9. Fachhochschulabsch |        34         21        148          0          0          0 |       203 
10. Hochschulabschlus |        89         11        297         40          1          0 |       438 
11. Keinen berufliche |         0          0          0          0        249          0 |       249 
        12. Sonstiges |         0          0          0          0        146          0 |       146 
                    . |         0          0          0          0         38          0 |        38 
                   .f |         0          0          0          0         12          0 |        12 
----------------------+------------------------------------------------------------------+----------
                Total |       123         32        445         40        446      2,192 |     3,278 

. replace educvocyr = educvocyr+univyr if univyr<.
(517 real changes made)

. tab educvocyr, mis

  RECODE of |
     stat17 |
   (Welchen |
   höchsten |
beruflichen |
Ausbildungs |
  abschluss |
haben Sie?) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        249        7.60        7.60
         .5 |         10        0.31        7.90
          1 |         85        2.59       10.49
          2 |        146        4.45       14.95
          3 |      1,998       60.95       75.90
          4 |        234        7.14       83.04
          5 |        318        9.70       92.74
          6 |        148        4.51       97.25
          9 |         40        1.22       98.47
          . |         38        1.16       99.63
         .f |         12        0.37      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. drop univyr

. 
. *All education in years
. gen educyr = educgenyr+educvocyr
(50 missing values generated)

. tab educyr, mis

     educyr |      Freq.     Percent        Cum.
------------+-----------------------------------
          7 |         34        1.04        1.04
        7.5 |          1        0.03        1.07
          8 |          7        0.21        1.28
          9 |        133        4.06        5.34
        9.5 |          1        0.03        5.37
         10 |         98        2.99        8.36
       10.5 |          5        0.15        8.51
         11 |         79        2.41       10.92
         12 |        725       22.12       33.04
         13 |      1,003       30.60       63.64
       13.5 |          3        0.09       63.73
         14 |        104        3.17       66.90
         15 |        179        5.46       72.36
         16 |        318        9.70       82.06
         17 |         79        2.41       84.47
         18 |        327        9.98       94.45
         19 |         92        2.81       97.25
         22 |         40        1.22       98.47
          . |         50        1.53      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Unemployed
. tab1 erw1 erw15h, mis

-> tabulation of erw1  

      Welche Beschäftigung traf vor der |
          Krise überwiegend auf Sie zu? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Vollzeit erwerbstätig (mind. 35h/Woc |      1,502       45.82       45.82
2. Teilzeit erwerbstätig (15h bis unter |        402       12.26       58.08
3. Geringfügig erwerbstätig (weniger al |         90        2.75       60.83
            4. Elternzeit, Mutterschutz |         51        1.56       62.39
                     5. Auszubildende/r |         26        0.79       63.18
              6. Schüler/in, Student/in |         67        2.04       65.22
7. Sozialer Freiwilligendienst, BFD, FS |          1        0.03       65.25
                          8. Arbeitslos |         78        2.38       67.63
                  9. Hausfrau, Hausmann |         90        2.75       70.38
           10. Rentner/in, Pensionär/in |        893       27.24       97.62
                          11. Sonstiges |         58        1.77       99.39
                                      . |         20        0.61      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of erw15h  

Erwerbssituation |
     im Moment - |
      Arbeitslos |      Freq.     Percent        Cum.
-----------------+-----------------------------------
0. Nicht Gewählt |      1,639       50.00       50.00
           1. Ja |         30        0.92       50.92
               . |         20        0.61       51.53
              .f |      1,589       48.47      100.00
-----------------+-----------------------------------
           Total |      3,278      100.00

. gen float unemployed = erw1==8

. replace unemployed = 1 if erw15h==1
(30 real changes made)

. tab unemployed, mis

 unemployed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,170       96.71       96.71
          1 |        108        3.29      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *Partner
. tab1 stat8 stat9, mis

-> tabulation of stat8  

             Wie ist Ihr Familienstand? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Verheiratet, mit Ehepartner/in zusam |      1,657       50.55       50.55
2. Eingetragene Partnerschaft (zusammen |        101        3.08       53.63
3. Verheiratet, dauerhaft getrennt lebe |         51        1.56       55.19
4. Eingetragene Partnerschaft (getrennt |         14        0.43       55.61
          5. Ledig, war nie verheiratet |        940       28.68       84.29
6. Geschieden / eingetragene Partnersch |        316        9.64       93.93
7. Verwitwet / Lebenspartner/in aus ein |        179        5.46       99.39
                                      . |         20        0.61      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,278      100.00

-> tabulation of stat9  

  Haben Sie |
    derzeit |
 eine feste |
Partnerscha |
        ft? |      Freq.     Percent        Cum.
------------+-----------------------------------
    0. Nein |        878       26.78       26.78
      1. Ja |        622       18.97       45.76
          . |         20        0.61       46.37
         .f |      1,758       53.63      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. gen float partner = (stat8==1 | stat8==2)

. tab partner

    partner |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,520       46.37       46.37
          1 |      1,758       53.63      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. replace partner=1 if stat9==1
(622 real changes made)

. tab partner, mis

    partner |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        898       27.39       27.39
          1 |      2,380       72.61      100.00
------------+-----------------------------------
      Total |      3,278      100.00

. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Generate empty vars for November wave vars
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. gen eastaffil=.
(3,278 missing values generated)

. gen fullossi=.
(3,278 missing values generated)

. gen lagbehind=.
(3,278 missing values generated)

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Recode infection rate variables
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. d kr*

              storage   display    value
variable name   type    format     label      variable label
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
kreis           str34   %34s                  
kr_inf_md       double  %8.0g                 Confirmed Corona cases by reporting date
kr_inf_md_kum   double  %8.0g                 Cumulated confirmed Corona cases by reporting date
kr_inf_aktiv    double  %8.0g                 Active Corona cases (cumcases minus cumdead minus cumrecovered)
kr_inz          double  %8.0g                 Confirmed Corona cases in last 7 days by reporting date
kr_inz_rate     double  %8.0g                 Confirmed Corona cases by 100000 residents in last 7 days by reporting date
kr_betr_rate    double  %8.0g                 Affection rate (cumcases/residents by reporting date)
kr_neuinf_rate  double  %8.0g                 New infection rate (new infections/cumcases by reporting date)
kr_tod_md       double  %10.0g                Corona deaths by reporting date
kr_tod_md_kum   double  %10.0g                Cumulated Corona deaths by reporting date
kr_sterbe_rate  double  %10.0g                Death rate (deathcum/infectionscum)
kr_population   double  %12.0g                Number of inhabitants in Kreis

. sum kr_inz_rate kr_betr_rate kr_tod_md_kum

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 kr_inz_rate |      3,204    4.514513    5.755875          0       80.3
kr_betr_rate |      3,204    .1879336    .1199164      .0294      .8974
kr_tod_md_~m |      3,204    49.72846    69.24207          0        264

. gen double kr_inz_rate10 = kr_inz_rate/10
(74 missing values generated)

. gen double kr_tod_md_kum10 = kr_tod_md_kum/10
(74 missing values generated)

. 
. gen double kr_tod_rate = (kr_tod_md_kum*100000)/kr_population
(74 missing values generated)

. gen double kr_tod_rate10 = (kr_tod_md_kum*10000)/kr_population
(74 missing values generated)

. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Save temporary dataset
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. gen float persweight = gewicht
(20 missing values generated)

. save springdatatemp.dta, replace
file springdatatemp.dta saved

. 
. 
. 
. 
end of do-file

. 
. *Prepare November 2020 survey
. do varprepnov.do

. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *
. * Diehl/Wolter: ATTITUDES ABOUT CONTAINMENT MEASURES DURING THE
. *               2020/2021 CORONAVIRUS PANDEMIC:
. *               SELF-INTEREST, OR BROADER POLITICAL ORIENTATIONS?
. *
. * Do-File for variable preparation of the November wave
. *
. * This version: 20210430
. *
. * Author: Felix Wolter, felix.wolter@uni-konstanz.de
. *
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. *Load November 2020 Kantar survey (data file with Covid rates has some missings to to missing PLZ!)
. use "kantarcovidratesnovember.dta", clear
(Corona-survey Cluster - Kantar W2)

. numlabel _all, add

. 
. *Drop Softlaunch
. tab sample

                        Umfrageversion |      Freq.     Percent        Cum.
---------------------------------------+-----------------------------------
1. Wiederholungsbefragung - Softlaunch |         81        2.47        2.47
             2. Wiederholungsbefragung |      2,651       80.77       83.24
                      3. Refreshsample |        550       16.76      100.00
---------------------------------------+-----------------------------------
                                 Total |      3,282      100.00

. drop if sample==1
(81 observations deleted)

. 
. *Wave (time) identifier
. gen time = 1

. label variable time "Time/wave"

. label define time 0 "spring 2020" 1 "november 2020"

. label value time time

. tab time, mis

    Time/wave |      Freq.     Percent        Cum.
--------------+-----------------------------------
november 2020 |      3,221      100.00      100.00
--------------+-----------------------------------
        Total |      3,221      100.00

. 
. *Rename Wave 1 identifier and repair id
. drop id

. rename id_w1 id

. replace id = id_w2 if id==.
(550 real changes made)

. replace id = 50000+_n if id==.
variable id was int now long
(20 real changes made)

. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Dependent variable
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. tab1 einst1-einst6, mis

-> tabulation of einst1  

    Richtiger Zeitpunkt Kitas schließen |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                1. nie1 |        529       16.42       16.42
2. erst bei sehr viel schnellerem Ansti |        991       30.77       47.19
 3. erst bei etwas schnellerem Anstieg3 |        610       18.94       66.13
4. erst bei weiterem Anstieg wie bisher |        683       21.20       87.33
                              5. jetzt5 |        381       11.83       99.16
                                      . |         27        0.84      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of einst2  

  Richtiger Zeitpunkt Schulen schließen |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                1. nie1 |        506       15.71       15.71
2. erst bei sehr viel schnellerem Ansti |      1,023       31.76       47.47
 3. erst bei etwas schnellerem Anstieg3 |        623       19.34       66.81
4. erst bei weiterem Anstieg wie bisher |        655       20.34       87.15
                              5. jetzt5 |        387       12.01       99.16
                                      . |         27        0.84      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of einst3  

    Richtiger Zeitpunkt Gaststätten und |
                  Restaurants schließen |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                1. nie1 |        598       18.57       18.57
2. erst bei sehr viel schnellerem Ansti |        878       27.26       45.82
 3. erst bei etwas schnellerem Anstieg3 |        510       15.83       61.66
4. erst bei weiterem Anstieg wie bisher |        475       14.75       76.40
                              5. jetzt5 |        732       22.73       99.13
                                      . |         28        0.87      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of einst4  

  Richtiger Zeitpunkt für Ausgangs- und |
                  Kontaktbeschränkungen |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                1. nie1 |        408       12.67       12.67
2. erst bei sehr viel schnellerem Ansti |        762       23.66       36.32
 3. erst bei etwas schnellerem Anstieg3 |        492       15.27       51.60
4. erst bei weiterem Anstieg wie bisher |        570       17.70       69.30
                              5. jetzt5 |        959       29.77       99.07
                                      . |         30        0.93      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of einst5  

  Richtiger Zeitpunkt Grenzen in Europa |
                              schließen |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                1. nie1 |        580       18.01       18.01
2. erst bei sehr viel schnellerem Ansti |        604       18.75       36.76
 3. erst bei etwas schnellerem Anstieg3 |        435       13.51       50.26
4. erst bei weiterem Anstieg wie bisher |        586       18.19       68.46
                              5. jetzt5 |        991       30.77       99.22
                                      . |         25        0.78      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of einst6  

                    Richtiger Zeitpunkt |
      Großveranstaltungen zu verbieten? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                1. nie1 |        154        4.78        4.78
2. erst bei sehr viel schnellerem Ansti |        396       12.29       17.08
 3. erst bei etwas schnellerem Anstieg3 |        382       11.86       28.94
4. erst bei weiterem Anstieg wie bisher |        370       11.49       40.42
                              5. jetzt5 |      1,897       58.89       99.32
                                      . |         22        0.68      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

. 
. foreach x of varlist einst1-einst6 {
  2.         gen `x'rec = 6-`x'
  3. }
(27 missing values generated)
(27 missing values generated)
(28 missing values generated)
(30 missing values generated)
(25 missing values generated)
(22 missing values generated)

. tab1 *rec, mis

-> tabulation of einst1rec  

  einst1rec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        381       11.83       11.83
          2 |        683       21.20       33.03
          3 |        610       18.94       51.97
          4 |        991       30.77       82.74
          5 |        529       16.42       99.16
          . |         27        0.84      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of einst2rec  

  einst2rec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        387       12.01       12.01
          2 |        655       20.34       32.35
          3 |        623       19.34       51.69
          4 |      1,023       31.76       83.45
          5 |        506       15.71       99.16
          . |         27        0.84      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of einst3rec  

  einst3rec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        732       22.73       22.73
          2 |        475       14.75       37.47
          3 |        510       15.83       53.31
          4 |        878       27.26       80.57
          5 |        598       18.57       99.13
          . |         28        0.87      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of einst4rec  

  einst4rec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        959       29.77       29.77
          2 |        570       17.70       47.47
          3 |        492       15.27       62.74
          4 |        762       23.66       86.40
          5 |        408       12.67       99.07
          . |         30        0.93      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of einst5rec  

  einst5rec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        991       30.77       30.77
          2 |        586       18.19       48.96
          3 |        435       13.51       62.47
          4 |        604       18.75       81.22
          5 |        580       18.01       99.22
          . |         25        0.78      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of einst6rec  

  einst6rec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,897       58.89       58.89
          2 |        370       11.49       70.38
          3 |        382       11.86       82.24
          4 |        396       12.29       94.54
          5 |        154        4.78       99.32
          . |         22        0.68      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. rename einst1rec openkitas

. rename einst2rec openschools

. rename einst3rec openrestau

. rename einst4rec opencontact

. rename einst5rec openborders

. rename einst6rec openevents

. 
. factor open* [aweight=gewicht], pcf blanks(.4)
(sum of wgt is 3,157.487)
(obs=3,165)

Factor analysis/correlation                      Number of obs    =      3,165
    Method: principal-component factors          Retained factors =          2
    Rotation: (unrotated)                        Number of params =         11

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      3.11645      1.96547            0.5194       0.5194
        Factor2  |      1.15098      0.40009            0.1918       0.7112
        Factor3  |      0.75089      0.25708            0.1251       0.8364
        Factor4  |      0.49381      0.10730            0.0823       0.9187
        Factor5  |      0.38651      0.28514            0.0644       0.9831
        Factor6  |      0.10137            .            0.0169       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(15) = 9342.22 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
       openkitas |   0.7743   -0.5858 |      0.0573  
     openschools |   0.7810   -0.5743 |      0.0603  
      openrestau |   0.7297           |      0.3907  
     opencontact |   0.7448    0.4230 |      0.2663  
     openborders |   0.5826           |      0.6469  
      openevents |   0.6930    0.4567 |      0.3111  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

. rotate, oblique oblimin blanks(.4)

Factor analysis/correlation                      Number of obs    =      3,165
    Method: principal-component factors          Retained factors =          2
    Rotation: oblique oblimin (Kaiser off)       Number of params =         11

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      2.66554       0.4443
        Factor2  |      2.40960       0.4016
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(15) = 9342.22 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
       openkitas |             0.9729 |      0.0573  
     openschools |             0.9653 |      0.0603  
      openrestau |   0.7249           |      0.3907  
     opencontact |   0.8631           |      0.2663  
     openborders |   0.4869           |      0.6469  
      openevents |   0.8586           |      0.3111  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

Factor rotation matrix

    --------------------------------
                 | Factor1  Factor2 
    -------------+------------------
         Factor1 |  0.8778   0.8002 
         Factor2 |  0.4790  -0.5997 
    --------------------------------

. 
. alpha open*, item detail

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
openkitas    | 3194    +       0.7486        0.6269        .7923916      0.7767
openschools  | 3194    +       0.7555        0.6369        .7889104      0.7748
openrestau   | 3193    +       0.7460        0.6019         .765545      0.7809
opencontact  | 3191    +       0.7597        0.6227         .755989      0.7760
openborders  | 3196    +       0.6311        0.4366        .8592966      0.8201
openevents   | 3199    +       0.7062        0.5693        .8243985      0.7885
-------------+-----------------------------------------------------------------
Test scale   |                                             .7977459      0.8154
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

               openkitas  openschools   openrestau  opencontact  openborders   openevents
  openkitas       1.6285
openschools       1.4627       1.6082
 openrestau       0.7697       0.7820       2.0888
opencontact       0.6620       0.6836       1.2073       2.0503
openborders       0.6245       0.6184       0.6498       0.7846       2.2760
 openevents       0.5239       0.5310       0.9026       1.0692       0.6964       1.6347

Pairwise number of observations

               openkitas  openschools   openrestau  opencontact  openborders   openevents
  openkitas         3194
openschools         3187         3194
 openrestau         3186         3186         3193
opencontact         3184         3184         3183         3191
openborders         3190         3190         3188         3186         3196
 openevents         3192         3193         3191         3189         3194         3199

. 
. *Generate index var of all 6 items
. egen openindex = rowmean(open*)
(20 missing values generated)

. tab openindex, mis

  openindex |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        119        3.69        3.69
   1.166667 |         49        1.52        5.22
   1.333333 |        103        3.20        8.41
        1.5 |        103        3.20       11.61
        1.6 |          3        0.09       11.70
   1.666667 |        118        3.66       15.37
        1.8 |          1        0.03       15.40
   1.833333 |        142        4.41       19.81
          2 |        213        6.61       26.42
   2.166667 |        157        4.87       31.29
        2.2 |          3        0.09       31.39
       2.25 |          1        0.03       31.42
   2.333333 |        190        5.90       37.32
        2.4 |          2        0.06       37.38
        2.5 |        183        5.68       43.06
        2.6 |          3        0.09       43.15
   2.666667 |        201        6.24       49.39
        2.8 |          2        0.06       49.46
   2.833333 |        183        5.68       55.14
          3 |        235        7.30       62.43
   3.166667 |        160        4.97       67.40
        3.2 |          1        0.03       67.43
       3.25 |          1        0.03       67.46
   3.333333 |        144        4.47       71.93
        3.4 |          1        0.03       71.97
        3.5 |        147        4.56       76.53
        3.6 |          1        0.03       76.56
   3.666667 |        151        4.69       81.25
        3.8 |          2        0.06       81.31
   3.833333 |        106        3.29       84.60
          4 |        125        3.88       88.48
   4.166667 |         80        2.48       90.97
        4.2 |          2        0.06       91.03
   4.333333 |         69        2.14       93.17
        4.4 |          1        0.03       93.20
        4.5 |         55        1.71       94.91
   4.666667 |         37        1.15       96.06
   4.833333 |         19        0.59       96.65
          5 |         88        2.73       99.38
          . |         20        0.62      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. sum openindex [aweight=gewicht]

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   openindex |   3,201    3197.148    2.742144   .9762322          1          5

. 
. *z-standardize dependent variable
. sum openindex [aweight=gewicht]

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   openindex |   3,201    3197.148    2.742144   .9762322          1          5

. center openindex [aweight=gewicht], gen(zopenindex) double nolabel standardize
(generated variables: zopenindex)

. center openindex , gen(znowopenindex) double nolabel standardize
(generated variables: znowopenindex)

. 
. *5-point-scale var
. gen openindex5 = openindex
(20 missing values generated)

. 
. *Copy five-point scale variable
. foreach x of varlist openkitas-openevents {
  2.         gen `x'5 = `x'
  3. }
(27 missing values generated)
(27 missing values generated)
(28 missing values generated)
(30 missing values generated)
(25 missing values generated)
(22 missing values generated)

. 
. *z-standardize openkitas and openschools for robustness analysis
. center openkitas [aweight=gewicht], gen(zopenkitas) double nolabel standardize
(generated variables: zopenkitas)

. center openschools [aweight=gewicht], gen(zopenschools) double nolabel standardize
(generated variables: zopenschools)

. 
. *Create opposition dummies for descriptive analysis
. foreach x of varlist openkitas5-openevents5 {
  2.     recode `x' (1/3=0) (4 5=1), gen(`x'dum)
  3. }
(3194 differences between openkitas5 and openkitas5dum)
(3194 differences between openschools5 and openschools5dum)
(3193 differences between openrestau5 and openrestau5dum)
(3191 differences between opencontact5 and opencontact5dum)
(3196 differences between openborders5 and openborders5dum)
(3199 differences between openevents5 and openevents5dum)

. tab1 *dum, mis

-> tabulation of openkitas5dum  

  RECODE of |
 openkitas5 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,674       51.97       51.97
          1 |      1,520       47.19       99.16
          . |         27        0.84      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of openschools5dum  

  RECODE of |
openschools |
          5 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,665       51.69       51.69
          1 |      1,529       47.47       99.16
          . |         27        0.84      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of openrestau5dum  

  RECODE of |
openrestau5 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,717       53.31       53.31
          1 |      1,476       45.82       99.13
          . |         28        0.87      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of opencontact5dum  

  RECODE of |
opencontact |
          5 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,021       62.74       62.74
          1 |      1,170       36.32       99.07
          . |         30        0.93      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of openborders5dum  

  RECODE of |
openborders |
          5 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,012       62.47       62.47
          1 |      1,184       36.76       99.22
          . |         25        0.78      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of openevents5dum  

  RECODE of |
openevents5 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,649       82.24       82.24
          1 |        550       17.08       99.32
          . |         22        0.68      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Independent variables
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Threats
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Sociotropic threat
. tab1 masn2a-masn2h, mis

-> tabulation of masn2a  

   Maßnahmen bedrohlich: |
  Arbeitsplatzsicherheit |
             Deutschland |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |         59        1.83        1.83
                    2. 2 |        174        5.40        7.23
                    3. 3 |        941       29.21       36.45
                    4. 4 |      1,161       36.04       72.49
     5. sehr bedrohlich5 |        861       26.73       99.22
                       . |         25        0.78      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2b  

   Maßnahmen bedrohlich: |
   finanzielle Situation |
             Deutschland |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |         39        1.21        1.21
                    2. 2 |        202        6.27        7.48
                    3. 3 |        835       25.92       33.41
                    4. 4 |      1,135       35.24       68.64
     5. sehr bedrohlich5 |        983       30.52       99.16
                       . |         27        0.84      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2c  

   Maßnahmen bedrohlich: |
      Situation Familien |
             Deutschland |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |         46        1.43        1.43
                    2. 2 |        323       10.03       11.46
                    3. 3 |      1,084       33.65       45.11
                    4. 4 |      1,133       35.18       80.29
     5. sehr bedrohlich5 |        603       18.72       99.01
                       . |         32        0.99      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2d  

   Maßnahmen bedrohlich: |
 Grundrechte Deutschland |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |        512       15.90       15.90
                    2. 2 |        720       22.35       38.25
                    3. 3 |        843       26.17       64.42
                    4. 4 |        526       16.33       80.75
     5. sehr bedrohlich5 |        594       18.44       99.19
                       . |         26        0.81      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2e  

   Maßnahmen bedrohlich: |
                  Eigene |
  Arbeitsplatzsicherheit |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |      1,162       36.08       36.08
                    2. 2 |        590       18.32       54.39
                    3. 3 |        689       21.39       75.78
                    4. 4 |        379       11.77       87.55
     5. sehr bedrohlich5 |        335       10.40       97.95
                       . |         66        2.05      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2f  

   Maßnahmen bedrohlich: |
      Eigene finanzielle |
               Situation |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |        723       22.45       22.45
                    2. 2 |        773       24.00       46.45
                    3. 3 |        843       26.17       72.62
                    4. 4 |        487       15.12       87.74
     5. sehr bedrohlich5 |        358       11.11       98.85
                       . |         37        1.15      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2g  

   Maßnahmen bedrohlich: |
        Eigene familiäre |
               Situation |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |        962       29.87       29.87
                    2. 2 |        890       27.63       57.50
                    3. 3 |        817       25.36       82.86
                    4. 4 |        334       10.37       93.23
     5. sehr bedrohlich5 |        179        5.56       98.79
                       . |         39        1.21      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2h  

   Maßnahmen bedrohlich: |
      Eigene Grundrechte |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |        764       23.72       23.72
                    2. 2 |        743       23.07       46.79
                    3. 3 |        766       23.78       70.57
                    4. 4 |        473       14.68       85.25
     5. sehr bedrohlich5 |        451       14.00       99.25
                       . |         24        0.75      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

. gen double sttwork = masn2a
(25 missing values generated)

. gen double sttfinance = masn2b
(27 missing values generated)

. gen double sttfamily = masn2c
(32 missing values generated)

. gen double sttrights = masn2d
(26 missing values generated)

. 
. pwcorr stt*, sig

             |  sttwork sttfin~e sttfam~y sttrig~s
-------------+------------------------------------
     sttwork |   1.0000 
             |
             |
  sttfinance |   0.6596   1.0000 
             |   0.0000
             |
   sttfamily |   0.5566   0.5774   1.0000 
             |   0.0000   0.0000
             |
   sttrights |   0.4386   0.4638   0.4937   1.0000 
             |   0.0000   0.0000   0.0000
             |

. factor stt* [aweight=gewicht], pcf blanks(.4)
(sum of wgt is 3,173.232)
(obs=3,175)

Factor analysis/correlation                      Number of obs    =      3,175
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          4

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.57738      1.95713            0.6443       0.6443
        Factor2  |      0.62025      0.16422            0.1551       0.7994
        Factor3  |      0.45603      0.10967            0.1140       0.9134
        Factor4  |      0.34635            .            0.0866       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(6)  = 4367.00 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
         sttwork |   0.8242 |      0.3208  
      sttfinance |   0.8474 |      0.2819  
       sttfamily |   0.8136 |      0.3380  
       sttrights |   0.7198 |      0.4819  
    ---------------------------------------
    (blanks represent abs(loading)<.4)

. alpha stt*, item detail

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
sttwork      | 3196    +       0.8022        0.6555        .5821953      0.7386
sttfinance   | 3194    +       0.8184        0.6805        .5637716      0.7272
sttfamily    | 3189    +       0.8006        0.6537        .5851927      0.7400
sttrights    | 3195    +       0.7918        0.5438        .5459525      0.8171
-------------+-----------------------------------------------------------------
Test scale   |                                             .5692813      0.8029
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

               sttwork  sttfinance   sttfamily   sttrights
   sttwork      0.9122
sttfinance      0.6039      0.9187
 sttfamily      0.5065      0.5274      0.9069
 sttrights      0.5587      0.5930      0.6262      1.7751

Pairwise number of observations

               sttwork  sttfinance   sttfamily   sttrights
   sttwork        3196
sttfinance        3190        3194
 sttfamily        3186        3183        3189
 sttrights        3191        3189        3184        3195

. 
. *Create index variable
. egen stt = rowmean(stt*)
(21 missing values generated)

. tab stt, mis

        stt |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          9        0.28        0.28
       1.25 |          5        0.16        0.43
        1.5 |         14        0.43        0.87
       1.75 |         43        1.33        2.20
          2 |         59        1.83        4.04
       2.25 |        104        3.23        7.26
   2.333333 |          1        0.03        7.30
        2.5 |        169        5.25       12.54
   2.666667 |          4        0.12       12.67
       2.75 |        259        8.04       20.71
          3 |        359       11.15       31.85
       3.25 |        334       10.37       42.22
   3.333333 |          2        0.06       42.29
        3.5 |        332       10.31       52.59
       3.75 |        331       10.28       62.87
          4 |        335       10.40       73.27
       4.25 |        212        6.58       79.85
   4.333333 |          4        0.12       79.98
        4.5 |        191        5.93       85.90
       4.75 |        145        4.50       90.41
          5 |        288        8.94       99.35
          . |         21        0.65      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Subindizes
. egen sttshort = rowmean(sttwork sttfinance sttfamily)
(21 missing values generated)

. egen sttecon = rowmean(sttwork sttfinance)
(21 missing values generated)

. 
. 
. *Individual threat
. tab1 masn2e-masn2h, mis

-> tabulation of masn2e  

   Maßnahmen bedrohlich: |
                  Eigene |
  Arbeitsplatzsicherheit |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |      1,162       36.08       36.08
                    2. 2 |        590       18.32       54.39
                    3. 3 |        689       21.39       75.78
                    4. 4 |        379       11.77       87.55
     5. sehr bedrohlich5 |        335       10.40       97.95
                       . |         66        2.05      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2f  

   Maßnahmen bedrohlich: |
      Eigene finanzielle |
               Situation |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |        723       22.45       22.45
                    2. 2 |        773       24.00       46.45
                    3. 3 |        843       26.17       72.62
                    4. 4 |        487       15.12       87.74
     5. sehr bedrohlich5 |        358       11.11       98.85
                       . |         37        1.15      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2g  

   Maßnahmen bedrohlich: |
        Eigene familiäre |
               Situation |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |        962       29.87       29.87
                    2. 2 |        890       27.63       57.50
                    3. 3 |        817       25.36       82.86
                    4. 4 |        334       10.37       93.23
     5. sehr bedrohlich5 |        179        5.56       98.79
                       . |         39        1.21      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

-> tabulation of masn2h  

   Maßnahmen bedrohlich: |
      Eigene Grundrechte |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
1. gar nicht bedrohlich1 |        764       23.72       23.72
                    2. 2 |        743       23.07       46.79
                    3. 3 |        766       23.78       70.57
                    4. 4 |        473       14.68       85.25
     5. sehr bedrohlich5 |        451       14.00       99.25
                       . |         24        0.75      100.00
-------------------------+-----------------------------------
                   Total |      3,221      100.00

. gen double idtwork = masn2e
(66 missing values generated)

. gen double idtfinance = masn2f
(37 missing values generated)

. gen double idtfamily = masn2g
(39 missing values generated)

. gen double idtrights = masn2h
(24 missing values generated)

. 
. pwcorr idt*, sig

             |  idtwork idtfin~e idtfam~y idtrig~s
-------------+------------------------------------
     idtwork |   1.0000 
             |
             |
  idtfinance |   0.7106   1.0000 
             |   0.0000
             |
   idtfamily |   0.4734   0.6043   1.0000 
             |   0.0000   0.0000
             |
   idtrights |   0.3777   0.4614   0.4746   1.0000 
             |   0.0000   0.0000   0.0000
             |

. factor idt* [aweight=gewicht], pcf blanks(.4)
(sum of wgt is 3,107.767)
(obs=3,122)

Factor analysis/correlation                      Number of obs    =      3,122
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          4

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.57912      1.92357            0.6448       0.6448
        Factor2  |      0.65556      0.15717            0.1639       0.8087
        Factor3  |      0.49839      0.23147            0.1246       0.9333
        Factor4  |      0.26693            .            0.0667       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(6)  = 4654.71 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
         idtwork |   0.8167 |      0.3329  
      idtfinance |   0.8813 |      0.2233  
       idtfamily |   0.7964 |      0.3657  
       idtrights |   0.7078 |      0.4990  
    ---------------------------------------
    (blanks represent abs(loading)<.4)

. alpha idt*, item detail

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
idtwork      | 3155    +       0.8095        0.6313        .8209943      0.7558
idtfinance   | 3184    +       0.8685        0.7446        .7349503      0.6990
idtfamily    | 3182    +       0.7843        0.6263        .9133993      0.7604
idtrights    | 3197    +       0.7316        0.5063        .9718262      0.8165
-------------+-----------------------------------------------------------------
Test scale   |                                             .8601695      0.8083
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

               idtwork  idtfinance   idtfamily   idtrights
   idtwork      1.8562
idtfinance      1.2462      1.6525
 idtfamily      0.7583      0.9113      1.3747
 idtrights      0.6953      0.8009      0.7511      1.8226

Pairwise number of observations

               idtwork  idtfinance   idtfamily   idtrights
   idtwork        3155
idtfinance        3141        3184
 idtfamily        3139        3166        3182
 idtrights        3152        3181        3179        3197

. 
. *Create index variable
. egen idt = rowmean(idt*)
(21 missing values generated)

. tab idt, mis

        idt |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        266        8.26        8.26
       1.25 |        191        5.93       14.19
   1.333333 |          5        0.16       14.34
        1.5 |        218        6.77       21.11
   1.666667 |         10        0.31       21.42
       1.75 |        241        7.48       28.90
          2 |        365       11.33       40.24
       2.25 |        239        7.42       47.66
   2.333333 |         10        0.31       47.97
        2.5 |        255        7.92       55.88
   2.666667 |          6        0.19       56.07
       2.75 |        240        7.45       63.52
          3 |        286        8.88       72.40
       3.25 |        179        5.56       77.96
   3.333333 |          8        0.25       78.21
        3.5 |        157        4.87       83.08
   3.666667 |          6        0.19       83.27
       3.75 |        133        4.13       87.40
          4 |        139        4.32       91.71
       4.25 |         67        2.08       93.79
   4.333333 |          3        0.09       93.88
        4.5 |         55        1.71       95.59
       4.75 |         32        0.99       96.58
          5 |         89        2.76       99.35
          . |         21        0.65      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Subindizes
. egen idtshort = rowmean(idtwork idtfinance idtfamily)
(21 missing values generated)

. egen idtecon = rowmean(idtwork idtfinance)
(23 missing values generated)

. 
. *Objective change in income due to corona
. tab1 erw8b erw16, mis

-> tabulation of erw8b  

   Wie hat sich Ihr Beitrag |
  zum Haushaltseinkommen im |
 Vergleich zur Zeit VOR der |
                         Co |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
          1. stark gesunken |        114        3.54        3.54
          2. etwas gesunken |        257        7.98       11.52
3. in etwa gleich geblieben |      1,536       47.69       59.21
         4. etwas gestiegen |        132        4.10       63.30
         5. stark gestiegen |         27        0.84       64.14
                          . |      1,155       35.86      100.00
----------------------------+-----------------------------------
                      Total |      3,221      100.00

-> tabulation of erw16  

  Haben Sie |
  wegen der |
Corona-Kris |
    e einen |
Einkommensv |
     erlust |
  hinnehmen |
    müssen? |      Freq.     Percent        Cum.
------------+-----------------------------------
    0. Nein |      1,225       38.03       38.03
      1. Ja |        383       11.89       49.92
          . |      1,613       50.08      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. gen double incloss = erw16==1 

. *replace incloss=0 if(erw2==4 |erw2==6) //Beamte and MFA have no loss
. *replace incloss=0 if erw2==5
. replace incloss=1 if(erw2==5 & (erw19==3 | erw19==4))
(47 real changes made)

. *replace incloss=0 if(erw1==8 | erw1==10) // Rentner and unemployed have no loss
. tab incloss, mis

    incloss |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,791       86.65       86.65
          1 |        430       13.35      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Political attitudes
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Left-Right-self positioning
. tab inst4, mis  //too much missings, don't use this variable!

      Politische |
Selbsteinstufung |
         'Links' |
       'Rechts'' |      Freq.     Percent        Cum.
-----------------+-----------------------------------
     0. 0(links) |         80        2.48        2.48
            1. 1 |         52        1.61        4.10
            2. 2 |        156        4.84        8.94
            3. 3 |        292        9.07       18.01
            4. 4 |        244        7.58       25.58
            5. 5 |        924       28.69       54.27
            6. 6 |        296        9.19       63.46
            7. 7 |        232        7.20       70.66
            8. 8 |        106        3.29       73.95
            9. 9 |         34        1.06       75.01
  10. 10(rechts) |         65        2.02       77.03
  11. weiß nicht |        354       10.99       88.02
12. keine Angabe |        360       11.18       99.19
               . |         26        0.81      100.00
-----------------+-----------------------------------
           Total |      3,221      100.00

. recast double inst4

. recode inst4 (11 12 .= .), gen(leftright)
(714 differences between inst4 and leftright)

. label var leftright "Left-Right-Self-Assess"

. tab leftright, mis

Left-Right- |
Self-Assess |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         80        2.48        2.48
          1 |         52        1.61        4.10
          2 |        156        4.84        8.94
          3 |        292        9.07       18.01
          4 |        244        7.58       25.58
          5 |        924       28.69       54.27
          6 |        296        9.19       63.46
          7 |        232        7.20       70.66
          8 |        106        3.29       73.95
          9 |         34        1.06       75.01
         10 |         65        2.02       77.03
          . |        740       22.97      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. gen double leftrightDK = inst4==11 if inst4~=.
(26 missing values generated)

. gen double leftrightNR = inst4==12 if inst4~=.
(26 missing values generated)

. gen double leftrightORIG = inst4
(26 missing values generated)

. tab1 leftrightDK leftrightNR, mis

-> tabulation of leftrightDK  

leftrightDK |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,841       88.20       88.20
          1 |        354       10.99       99.19
          . |         26        0.81      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of leftrightNR  

leftrightNR |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,835       88.02       88.02
          1 |        360       11.18       99.19
          . |         26        0.81      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. recode inst4 (0/3=1) (4/6=2) (7/10=3) (11/12=4), gen(leftrightcat)
(3143 differences between inst4 and leftrightcat)

. label variable leftrightcat "Left-Right categorical"

. label define lericat 1 "left" 2 "mid" 3 "right" 4 "DK and NR"

. label value leftrightcat lericat

. numlabel lericat, add

. tab leftrightcat, mis

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |        580       18.01       18.01
      2. mid |      1,464       45.45       63.46
    3. right |        437       13.57       77.03
4. DK and NR |        714       22.17       99.19
           . |         26        0.81      100.00
-------------+-----------------------------------
       Total |      3,221      100.00

. 
. *Party preference
. tab1 inst6*, mis

-> tabulation of inst6  

        Politische Neigung |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
              1. CDU / CSU |        669       20.77       20.77
                    2. SPD |        459       14.25       35.02
                    3. FDP |        113        3.51       38.53
              4. Die Linke |        194        6.02       44.55
                    5. AfD |        217        6.74       51.29
6. Bündnis 90 - Die Grünen |        274        8.51       59.80
          7. Keiner Partei |        593       18.41       78.21
              8. Sonstiges |         59        1.83       80.04
           9. Keine Angabe |        612       19.00       99.04
                         . |         31        0.96      100.00
---------------------------+-----------------------------------
                     Total |      3,221      100.00

-> tabulation of inst6_other  

         Politische Neigung - Sonstiges |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                        |         20        0.62        0.62
                                .FILTER |      3,142       97.55       98.17
     Basisdemokratische Partei, WIR2020 |          1        0.03       98.20
Christliche Partei Deutschlands= CPD .. |          1        0.03       98.23
            Das BÃŒndnis Grundeinkommen |          1        0.03       98.26
                              Die Liebe |          1        0.03       98.29
                             Die PARTEI |          3        0.09       98.39
                             Die Partei |         13        0.40       98.79
                            Die Piraten |          1        0.03       98.82
                            Die Rechten |          1        0.03       98.85
                     Die rechte.  3 weg |          1        0.03       98.88
                                     FW |          1        0.03       98.91
                          Freie WÃ€hler |          4        0.12       99.04
                                  Graue |          1        0.03       99.07
                             Humanisten |          1        0.03       99.10
                                    NPD |          4        0.12       99.22
                                Piraten |          2        0.06       99.29
                          Piratenpartei |          2        0.06       99.35
                             Tierpartei |          1        0.03       99.38
                             Tierschutz |          5        0.16       99.53
                       Tierschutzpartei |          3        0.09       99.63
                   TierschÃŒtzer Partei |          1        0.03       99.66
                              UngÃŒltig |          1        0.03       99.69
                                   Volt |          1        0.03       99.72
                                   alle |          1        0.03       99.75
                             die Partei |          1        0.03       99.78
                             die grauen |          1        0.03       99.81
                                    npd |          1        0.03       99.84
                                piraten |          1        0.03       99.88
                       tierschutzpartei |          1        0.03       99.91
                                   ÃDP |          3        0.09      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

. gen other = inst6_other~=".FILTER"

. tab other, mis

      other |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,142       97.55       97.55
          1 |         79        2.45      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. replace inst6 = 7 if other==1
(79 real changes made)

. gen double partypref = inst6
(11 missing values generated)

. label define inst6 7 "keine/andere Partei", modify

. label value partypref inst6

. tab partypref, mis

                 partypref |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
              1. CDU / CSU |        669       20.77       20.77
                    2. SPD |        459       14.25       35.02
                    3. FDP |        113        3.51       38.53
              4. Die Linke |        194        6.02       44.55
                    5. AfD |        217        6.74       51.29
6. Bündnis 90 - Die Grünen |        274        8.51       59.80
       keine/andere Partei |        672       20.86       80.66
           9. Keine Angabe |        612       19.00       99.66
                         . |         11        0.34      100.00
---------------------------+-----------------------------------
                     Total |      3,221      100.00

. drop other

. 
. 
. *Trust in institutions
. tab1 inst1a, mis

-> tabulation of inst1a  

      Vertrauen Institutionen - |
                Bundesregierung |      Freq.     Percent        Cum.
--------------------------------+-----------------------------------
1. 1 - überhaupt kein Vertrauen |        358       11.11       11.11
                           2. 2 |        222        6.89       18.01
                           3. 3 |        420       13.04       31.05
                           4. 4 |        523       16.24       47.28
                           5. 5 |        680       21.11       68.39
                           6. 6 |        730       22.66       91.06
   7. 7 - sehr großes Vertrauen |        260        8.07       99.13
                              . |         28        0.87      100.00
--------------------------------+-----------------------------------
                          Total |      3,221      100.00

. rename inst1a trustbundesreg

. rename inst1b trustbundestag

. rename inst1c trustlandreg

. rename inst1d trustparties

. rename inst1e trusttelly

. rename inst1f trustjournals

. rename inst1g trustsocmed

. rename inst1h trusthealthsys

. rename inst1i trustpolice

. rename inst1j trustscience

. 
. recast double trust*

. 
. factor trust* [aweight=gewicht], pcf blanks(.4)
(sum of wgt is 3,120.138)
(obs=3,122)

Factor analysis/correlation                      Number of obs    =      3,122
    Method: principal-component factors          Retained factors =          2
    Rotation: (unrotated)                        Number of params =         19

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      6.35731      5.34281            0.6357       0.6357
        Factor2  |      1.01450      0.27556            0.1014       0.7372
        Factor3  |      0.73894      0.19907            0.0739       0.8111
        Factor4  |      0.53987      0.15433            0.0540       0.8651
        Factor5  |      0.38555      0.08182            0.0386       0.9036
        Factor6  |      0.30372      0.03411            0.0304       0.9340
        Factor7  |      0.26961      0.08222            0.0270       0.9610
        Factor8  |      0.18740      0.07978            0.0187       0.9797
        Factor9  |      0.10761      0.01212            0.0108       0.9905
       Factor10  |      0.09549            .            0.0095       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(45) = 2.7e+04 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
    trustbund~eg |   0.8975           |      0.1737  
    trustbund~ag |   0.8915           |      0.1925  
    trustlandreg |   0.8656           |      0.2391  
    trustparties |   0.8250           |      0.3119  
      trusttelly |   0.8419           |      0.2087  
    trustjourn~s |   0.8325           |      0.2224  
     trustsocmed |   0.4455    0.7746 |      0.2015  
    trusthealt~s |   0.7880           |      0.3254  
     trustpolice |   0.7170           |      0.4276  
    trustscience |   0.7692           |      0.3255  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

. rotate, oblique oblimin blanks(.4)  //different results than in the spring wave!!

Factor analysis/correlation                      Number of obs    =      3,122
    Method: principal-component factors          Retained factors =          2
    Rotation: oblique oblimin (Kaiser off)       Number of params =         19

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      6.21435       0.6214
        Factor2  |      2.53917       0.2539
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(45) = 2.7e+04 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
    trustbund~eg |   0.9070           |      0.1737  
    trustbund~ag |   0.8833           |      0.1925  
    trustlandreg |   0.8568           |      0.2391  
    trustparties |   0.7068           |      0.3119  
      trusttelly |   0.6060    0.4569 |      0.2087  
    trustjourn~s |   0.5952    0.4592 |      0.2224  
     trustsocmed |             0.9085 |      0.2015  
    trusthealt~s |   0.8573           |      0.3254  
     trustpolice |   0.7978           |      0.4276  
    trustscience |   0.8726           |      0.3255  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

Factor rotation matrix

    --------------------------------
                 | Factor1  Factor2 
    -------------+------------------
         Factor1 |  0.9865   0.5342 
         Factor2 | -0.1636   0.8454 
    --------------------------------

. 
. *Test subdimensions for uni-dimensionality
. factor trustbundes* trustland trustparties trusthealthsys trustpolice trustscience ///
>    [aweight=gewicht] , pcf blanks(.4) //yep
(sum of wgt is 3,140.318)
(obs=3,143)

Factor analysis/correlation                      Number of obs    =      3,143
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          7

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      4.95788      4.19603            0.7083       0.7083
        Factor2  |      0.76184      0.36282            0.1088       0.8171
        Factor3  |      0.39902      0.08769            0.0570       0.8741
        Factor4  |      0.31133      0.03308            0.0445       0.9186
        Factor5  |      0.27825      0.08833            0.0397       0.9583
        Factor6  |      0.18992      0.08814            0.0271       0.9855
        Factor7  |      0.10178            .            0.0145       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(21) = 1.9e+04 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
    trustbund~eg |   0.9166 |      0.1598  
    trustbund~ag |   0.9103 |      0.1713  
    trustlandreg |   0.8843 |      0.2180  
    trustparties |   0.8208 |      0.3263  
    trusthealt~s |   0.8101 |      0.3437  
     trustpolice |   0.7408 |      0.4512  
    trustscience |   0.7926 |      0.3719  
    ---------------------------------------
    (blanks represent abs(loading)<.4)

. 
. factor trusttelly trustjournals trustsocmed ///
>    [aweight=gewicht] , pcf blanks(.4)  //yep, but this doesn't make sense
(sum of wgt is 3,171.174)
(obs=3,175)

Factor analysis/correlation                      Number of obs    =      3,175
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          3

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.23208      1.56748            0.7440       0.7440
        Factor2  |      0.66460      0.56129            0.2215       0.9656
        Factor3  |      0.10332            .            0.0344       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(3)  = 5951.60 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
      trusttelly |   0.9371 |      0.1218  
    trustjourn~s |   0.9361 |      0.1237  
     trustsocmed |   0.6911 |      0.5224  
    ---------------------------------------
    (blanks represent abs(loading)<.4)

. 
. *Reliability
. alpha trustbundes* trustland trustparties trusthealthsys trustpolice trustscience, item detail

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
trustbund~eg | 3193    +       0.9141        0.8729        1.574028      0.9091
trustbund~ag | 3190    +       0.9082        0.8677        1.616118      0.9097
trustlandreg | 3183    +       0.8812        0.8289        1.634913      0.9136
trustparties | 3186    +       0.8107        0.7454        1.782115      0.9220
trusthealt~s | 3194    +       0.8018        0.7342        1.792108      0.9230
trustpolice  | 3192    +       0.7480        0.6572        1.806646      0.9301
trustscience | 3194    +       0.7993        0.7255        1.765497      0.9237
-------------+-----------------------------------------------------------------
Test scale   |                                             1.710192      0.9298
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

                trustbundesreg  trustbundestag    trustlandreg    trustparties  trusthealthsys     trustpolice    trustscience
trustbundesreg          3.1855
trustbundestag          2.6921          2.8270
  trustlandreg          2.5497          2.3485          2.9222
  trustparties          1.9472          1.9210          1.8187          2.1753
trusthealthsys          1.6776          1.5379          1.5532          1.1671          2.1635
   trustpolice          1.6171          1.5013          1.4892          1.1571          1.5074          2.5666
  trustscience          1.8202          1.6731          1.6348          1.1709          1.5904          1.5427          2.4581

Pairwise number of observations

                trustbundesreg  trustbundestag    trustlandreg    trustparties  trusthealthsys     trustpolice    trustscience
trustbundesreg            3193
trustbundestag            3184            3190
  trustlandreg            3176            3173            3183
  trustparties            3180            3178            3170            3186
trusthealthsys            3188            3186            3177            3181            3194
   trustpolice            3186            3183            3175            3180            3188            3192
  trustscience            3188            3185            3177            3181            3190            3188            3194

. alpha trusttelly trustjournals trustsocmed, item detail  //social media problematic

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
trusttelly   | 3192    +       0.9254        0.8073        1.054332      0.6241
trustjourn~s | 3194    +       0.9234        0.8056        1.078026      0.6257
trustsocmed  | 3187    +       0.7238        0.4699        2.358029      0.9454
-------------+-----------------------------------------------------------------
Test scale   |                                             1.497334      0.8264
-------------------------------------------------------------------------------

Interitem covariances (obs=pairwise, see below)

                  trusttelly  trustjournals    trustsocmed
   trusttelly         2.6738
trustjournals         2.3580         2.5871
  trustsocmed         1.0780         1.0543         2.0610

Pairwise number of observations

                  trusttelly  trustjournals    trustsocmed
   trusttelly           3192
trustjournals           3187           3194
  trustsocmed           3180           3182           3187

. 
. *Generate index variables
. egen truststate = rowmean(trustbundes* trustland trustparties trusthealthsys trustpolice trustscience)
(21 missing values generated)

. egen trustmedia = rowmean(trusttelly trustjournals trustsocmed)
(22 missing values generated)

. egen trustpress = rowmean(trusttelly trustjournals)
(22 missing values generated)

. tab1 truststate trustmedia trustpress

-> tabulation of truststate  

 truststate |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         43        1.34        1.34
   1.142857 |          3        0.09        1.44
   1.285714 |         19        0.59        2.03
   1.333333 |          1        0.03        2.06
   1.428571 |         28        0.88        2.94
   1.571429 |         27        0.84        3.78
   1.714286 |         16        0.50        4.28
   1.857143 |         31        0.97        5.25
          2 |         28        0.88        6.13
   2.142857 |         43        1.34        7.47
   2.166667 |          1        0.03        7.50
   2.285714 |         46        1.44        8.94
   2.333333 |          1        0.03        8.97
   2.428571 |         45        1.41       10.38
   2.571429 |         56        1.75       12.13
   2.666667 |          2        0.06       12.19
   2.714286 |         48        1.50       13.69
   2.857143 |         44        1.38       15.06
          3 |         77        2.41       17.47
   3.142857 |         63        1.97       19.44
   3.285714 |         71        2.22       21.66
   3.333333 |          1        0.03       21.69
   3.428571 |         63        1.97       23.66
        3.5 |          1        0.03       23.69
   3.571429 |         71        2.22       25.91
   3.714286 |         62        1.94       27.84
   3.833333 |          4        0.13       27.97
   3.857143 |         86        2.69       30.66
          4 |        171        5.34       36.00
   4.142857 |        105        3.28       39.28
   4.166667 |          3        0.09       39.38
   4.285714 |        113        3.53       42.91
   4.333333 |          1        0.03       42.94
   4.428571 |        115        3.59       46.53
        4.5 |          2        0.06       46.59
   4.571429 |        106        3.31       49.91
   4.666667 |          4        0.13       50.03
   4.714286 |        127        3.97       54.00
        4.8 |          1        0.03       54.03
   4.833333 |          3        0.09       54.13
   4.857143 |        115        3.59       57.72
          5 |        166        5.19       62.91
   5.142857 |        126        3.94       66.84
   5.166667 |          2        0.06       66.91
   5.285714 |        126        3.94       70.84
   5.333333 |          3        0.09       70.94
        5.4 |          1        0.03       70.97
   5.428571 |        135        4.22       75.19
        5.5 |          4        0.13       75.31
   5.571429 |        122        3.81       79.13
   5.666667 |          2        0.06       79.19
   5.714286 |        130        4.06       83.25
   5.833333 |          3        0.09       83.34
   5.857143 |        117        3.66       87.00
          6 |        116        3.63       90.63
   6.142857 |         88        2.75       93.38
   6.166667 |          2        0.06       93.44
   6.285714 |         60        1.88       95.31
        6.4 |          1        0.03       95.34
   6.428571 |         41        1.28       96.63
        6.5 |          2        0.06       96.69
   6.571429 |         30        0.94       97.63
   6.714286 |         25        0.78       98.41
   6.857143 |         13        0.41       98.81
          7 |         38        1.19      100.00
------------+-----------------------------------
      Total |      3,200      100.00

-> tabulation of trustmedia  

 trustmedia |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        217        6.78        6.78
   1.333333 |         61        1.91        8.69
        1.5 |          1        0.03        8.72
   1.666667 |        123        3.84       12.57
          2 |        233        7.28       19.85
   2.333333 |        140        4.38       24.23
        2.5 |          4        0.13       24.35
   2.666667 |        158        4.94       29.29
          3 |        313        9.78       39.07
   3.333333 |        210        6.56       45.64
        3.5 |          4        0.13       45.76
   3.666667 |        243        7.60       53.36
          4 |        499       15.60       68.96
   4.333333 |        217        6.78       75.74
        4.5 |          2        0.06       75.80
   4.666667 |        225        7.03       82.84
          5 |        252        7.88       90.72
   5.333333 |        115        3.59       94.31
   5.666667 |         55        1.72       96.03
          6 |         76        2.38       98.41
   6.333333 |         15        0.47       98.87
   6.666667 |          6        0.19       99.06
          7 |         30        0.94      100.00
------------+-----------------------------------
      Total |      3,199      100.00

-> tabulation of trustpress  

 trustpress |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        314        9.82        9.82
        1.5 |         60        1.88       11.69
          2 |        300        9.38       21.07
        2.5 |        112        3.50       24.57
          3 |        408       12.75       37.32
        3.5 |        150        4.69       42.01
          4 |        650       20.32       62.33
        4.5 |        176        5.50       67.83
          5 |        492       15.38       83.21
        5.5 |        118        3.69       86.90
          6 |        300        9.38       96.28
        6.5 |         26        0.81       97.09
          7 |         93        2.91      100.00
------------+-----------------------------------
      Total |      3,199      100.00

. 
. gen doubtstate = 8-truststate
(21 missing values generated)

. gen doubtmedia = 8-trustmedia
(22 missing values generated)

. gen doubtpress = 8-trustpress
(22 missing values generated)

. gen doubtsocmed = 8-trustsocmed
(34 missing values generated)

. 
. 
. *How truthfully has the government informed?
. tab inst3, mis

Wie wahrheitsgetreu hat Bundesreg. |
             über den Ausbruch des |
           Corona-Virus informiert |      Freq.     Percent        Cum.
-----------------------------------+-----------------------------------
1. überhaupt nicht wahrheitsgetreu |        324       10.06       10.06
          2. wenig wahrheitsgetreu |        614       19.06       29.12
                     3. weder noch |        599       18.60       47.72
       4. ziemlich wahrheitsgetreu |      1,328       41.23       88.95
           5. sehr wahrheitsgetreu |        328       10.18       99.13
                                 . |         28        0.87      100.00
-----------------------------------+-----------------------------------
                             Total |      3,221      100.00

. recast double inst3

. gen doubtgovtruth = 6-inst3
(28 missing values generated)

. tab doubtgovtruth, mis

doubtgovtru |
         th |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        328       10.18       10.18
          2 |      1,328       41.23       51.41
          3 |        599       18.60       70.01
          4 |        614       19.06       89.07
          5 |        324       10.06       99.13
          . |         28        0.87      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. 
. *General trust
. tab inst8, mis

       Vertrauen im Umgang mit Menschen |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
0. 0 - Man kann nicht vorsichtig genug  |        321        9.97        9.97
                                   1. 1 |        203        6.30       16.27
                                   2. 2 |        364       11.30       27.57
                                   3. 3 |        428       13.29       40.86
                                   4. 4 |        297        9.22       50.08
                                   5. 5 |        539       16.73       66.81
                                   6. 6 |        288        8.94       75.75
                                   7. 7 |        364       11.30       87.05
                                   8. 8 |        289        8.97       96.03
                                   9. 9 |         57        1.77       97.80
10. 10 - Man kann den Menschen vertraue |         44        1.37       99.16
                                      . |         27        0.84      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

. recast double inst8

. gen gentrust = inst8
(27 missing values generated)

. tab gentrust, mis

   gentrust |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        321        9.97        9.97
          1 |        203        6.30       16.27
          2 |        364       11.30       27.57
          3 |        428       13.29       40.86
          4 |        297        9.22       50.08
          5 |        539       16.73       66.81
          6 |        288        8.94       75.75
          7 |        364       11.30       87.05
          8 |        289        8.97       96.03
          9 |         57        1.77       97.80
         10 |         44        1.37       99.16
          . |         27        0.84      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Communism cleavage, strong transfer state
. tab umv4, mis

  Wie viel Umverteilung der |
  Einkommen befürworten Sie |
    zwischen den Bürgern in |
                       Dtld |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
  0. 0 - keine Umverteilung |        505       15.68       15.68
                       1. 1 |        189        5.87       21.55
                       2. 2 |        325       10.09       31.64
                       3. 3 |        309        9.59       41.23
                       4. 4 |        233        7.23       48.46
                       5. 5 |        664       20.61       69.08
                       6. 6 |        312        9.69       78.76
                       7. 7 |        290        9.00       87.77
                       8. 8 |        193        5.99       93.76
                       9. 9 |         40        1.24       95.00
10. 10 - volle Umverteilung |        118        3.66       98.67
                          . |         43        1.33      100.00
----------------------------+-----------------------------------
                      Total |      3,221      100.00

. gen double protransfer = umv4
(43 missing values generated)

. tab protransfer, mis

protransfer |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        505       15.68       15.68
          1 |        189        5.87       21.55
          2 |        325       10.09       31.64
          3 |        309        9.59       41.23
          4 |        233        7.23       48.46
          5 |        664       20.61       69.08
          6 |        312        9.69       78.76
          7 |        290        9.00       87.77
          8 |        193        5.99       93.76
          9 |         40        1.24       95.00
         10 |        118        3.66       98.67
          . |         43        1.33      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Covid risk group
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. *Trust in health system if infected
. tab welf5, mis

       Vertrauen in |
  Gesundheitssystem |
   bei Corona, dass |
      man benötigte |
 Versorgung bekommt |      Freq.     Percent        Cum.
--------------------+-----------------------------------
   1. 1 - sehr hoch |        829       25.74       25.74
               2. 2 |      1,302       40.42       66.16
               3. 3 |        738       22.91       89.07
               4. 4 |        211        6.55       95.62
5. 5 - sehr niedrig |        114        3.54       99.16
                  . |         27        0.84      100.00
--------------------+-----------------------------------
              Total |      3,221      100.00

. recast double welf5

. gen trusthsyscorona = 6-welf5
(27 missing values generated)

. gen doubthsyscorona = welf5
(27 missing values generated)

. tab1 trusthsyscorona doubthsyscorona, mis

-> tabulation of trusthsyscorona  

trusthsysco |
       rona |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        114        3.54        3.54
          2 |        211        6.55       10.09
          3 |        738       22.91       33.00
          4 |      1,302       40.42       73.42
          5 |        829       25.74       99.16
          . |         27        0.84      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of doubthsyscorona  

doubthsysco |
       rona |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        829       25.74       25.74
          2 |      1,302       40.42       66.16
          3 |        738       22.91       89.07
          4 |        211        6.55       95.62
          5 |        114        3.54       99.16
          . |         27        0.84      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Risk group
. tab ges4 , mis //problem: many refusals

    Gehören Sie |
   aufgrund von |
Vorerkrankungen |
            zur |
Corona-Risikogr |
          uppe? |      Freq.     Percent        Cum.
----------------+-----------------------------------
        0. Nein |      1,850       57.44       57.44
          1. Ja |      1,235       38.34       95.78
9. Keine Angabe |        111        3.45       99.22
              . |         25        0.78      100.00
----------------+-----------------------------------
          Total |      3,221      100.00

. gen float riskgroup01 = ges4 if(ges4~=9 & ges4~=.)
(136 missing values generated)

. gen float riskgroup = ges4
(25 missing values generated)

. gen float riskgroupmiss = ges4==9 if ges4~=.
(25 missing values generated)

. tab1 riskgroup*, mis

-> tabulation of riskgroup01  

riskgroup01 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,850       57.44       57.44
          1 |      1,235       38.34       95.78
          . |        136        4.22      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of riskgroup  

  riskgroup |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,850       57.44       57.44
          1 |      1,235       38.34       95.78
          9 |        111        3.45       99.22
          . |         25        0.78      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of riskgroupmiss  

riskgroupmi |
         ss |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,085       95.78       95.78
          1 |        111        3.45       99.22
          . |         25        0.78      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *General health
. tab ges1, mis

       Wie würden Sie Ihren derzeitigen |
        Gesundheitszustand beschreiben? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Mir geht es gesundheitlich sehr gut. |        640       19.87       19.87
     2. Mir geht es gesundheitlich gut. |      1,092       33.90       53.77
3. Ich habe kleinere Beschwerden, mit d |        853       26.48       80.25
4. Ich habe gesundheitliche Probleme, d |        467       14.50       94.75
5. Ich leide an einer ernsthaften Krank |        111        3.45       98.20
           6. Möchte ich nicht angeben. |         38        1.18       99.38
                                      . |         20        0.62      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

. gen float health = 6-ges1 if ges1<6
(58 missing values generated)

. tab health, mis

     health |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        111        3.45        3.45
          2 |        467       14.50       17.94
          3 |        853       26.48       44.43
          4 |      1,092       33.90       78.33
          5 |        640       19.87       98.20
          . |         58        1.80      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. gen badhealth = 6-health
(58 missing values generated)

. tab badhealth

  badhealth |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        640       20.23       20.23
          2 |      1,092       34.52       54.76
          3 |        853       26.97       81.73
          4 |        467       14.76       96.49
          5 |        111        3.51      100.00
------------+-----------------------------------
      Total |      3,163      100.00

. 
. *Contact with infected people
. tab1 ges3*, mis

-> tabulation of ges3  

  Jemand im |
Bekanntenkr |
   eis, der |
positiv auf |
Corona-Viru |
 s getestet |      Freq.     Percent        Cum.
------------+-----------------------------------
    0. Nein |      2,174       67.49       67.49
      1. Ja |      1,015       31.51       99.01
          . |         32        0.99      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of ges3_other  

    Jemand im |
Bekanntenkrei |
       s, der |
  positiv auf |
 Corona-Virus |
   getestet - |
       Anzahl |      Freq.     Percent        Cum.
--------------+-----------------------------------
              |      2,206       68.49       68.49
            1 |        303        9.41       77.90
     1 Person |          1        0.03       77.93
           10 |         29        0.90       78.83
           12 |          2        0.06       78.89
           15 |          4        0.12       79.01
           17 |          1        0.03       79.04
           18 |          1        0.03       79.07
            2 |        310        9.62       88.70
           20 |          5        0.16       88.85
           25 |          1        0.03       88.89
            3 |        144        4.47       93.36
          3-5 |          1        0.03       93.39
           30 |          1        0.03       93.42
        31275 |          1        0.03       93.45
            4 |         82        2.55       96.00
            5 |         72        2.24       98.23
           50 |          1        0.03       98.26
            6 |         20        0.62       98.88
            7 |         10        0.31       99.19
            8 |         16        0.50       99.69
            9 |          4        0.12       99.81
         Ca.6 |          1        0.03       99.84
Mindestens 10 |          1        0.03       99.88
     bis zu 5 |          1        0.03       99.91
        ca 10 |          1        0.03       99.94
        ca. 6 |          1        0.03       99.97
         ca.5 |          1        0.03      100.00
--------------+-----------------------------------
        Total |      3,221      100.00

. gen float contact = ges3
(32 missing values generated)

. replace contact = 1 if (ges3_other~="")
(0 real changes made)

. replace contact = . if(ges3==. & ges3_other==".")
(0 real changes made)

. tab contact, mis 

    contact |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,174       67.49       67.49
          1 |      1,015       31.51       99.01
          . |         32        0.99      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Sociodemographics and other
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Life satisfaction
. tab zufr1, mis

          Wie zufrieden sind Sie |
    gegenwärtig, alles in allem, |
                mit Ihrem Leben? |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
0. 0 - überhaupt nicht zufrieden |         42        1.30        1.30
                            1. 1 |         26        0.81        2.11
                            2. 2 |         77        2.39        4.50
                            3. 3 |        153        4.75        9.25
                            4. 4 |        157        4.87       14.13
                            5. 5 |        363       11.27       25.40
                            6. 6 |        331       10.28       35.67
                            7. 7 |        679       21.08       56.75
                            8. 8 |        806       25.02       81.78
                            9. 9 |        358       11.11       92.89
       10. 10 - völlig zufrieden |        202        6.27       99.16
                               . |         27        0.84      100.00
---------------------------------+-----------------------------------
                           Total |      3,221      100.00

. gen double lsatisfac = zufr1
(27 missing values generated)

. tab lsatisfac, mis

  lsatisfac |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         42        1.30        1.30
          1 |         26        0.81        2.11
          2 |         77        2.39        4.50
          3 |        153        4.75        9.25
          4 |        157        4.87       14.13
          5 |        363       11.27       25.40
          6 |        331       10.28       35.67
          7 |        679       21.08       56.75
          8 |        806       25.02       81.78
          9 |        358       11.11       92.89
         10 |        202        6.27       99.16
          . |         27        0.84      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Sex
. tab stat1, mis

Sind Sie... |      Freq.     Percent        Cum.
------------+-----------------------------------
0. Männlich |      1,559       48.40       48.40
1. Weiblich |      1,635       50.76       99.16
  2. Divers |          7        0.22       99.38
          . |         20        0.62      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. recast float stat1

. recode stat1 (0=0) (1 2=1), gen(female)
(7 differences between stat1 and female)

. label var female ""

. tab female, mis

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,559       48.40       48.40
          1 |      1,642       50.98       99.38
          . |         20        0.62      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Age
. tab1 stat2*, mis

-> tabulation of stat2  

 In welchem Jahr sind |
         Sie geboren? |      Freq.     Percent        Cum.
----------------------+-----------------------------------
           1930. 1930 |          1        0.03        0.03
           1931. 1931 |          1        0.03        0.06
           1933. 1933 |          3        0.09        0.16
           1934. 1934 |          1        0.03        0.19
           1935. 1935 |          6        0.19        0.37
           1936. 1936 |          2        0.06        0.43
           1937. 1937 |         10        0.31        0.75
           1938. 1938 |         14        0.43        1.18
           1939. 1939 |         11        0.34        1.52
           1940. 1940 |         24        0.75        2.27
           1941. 1941 |         26        0.81        3.07
           1942. 1942 |         21        0.65        3.73
           1943. 1943 |         27        0.84        4.56
           1944. 1944 |         26        0.81        5.37
           1945. 1945 |         18        0.56        5.93
           1946. 1946 |         29        0.90        6.83
           1947. 1947 |         56        1.74        8.57
           1948. 1948 |         53        1.65       10.21
           1949. 1949 |         83        2.58       12.79
           1950. 1950 |         61        1.89       14.68
           1951. 1951 |         64        1.99       16.67
           1952. 1952 |         63        1.96       18.63
           1953. 1953 |         58        1.80       20.43
           1954. 1954 |         86        2.67       23.10
           1955. 1955 |         73        2.27       25.36
           1956. 1956 |         53        1.65       27.01
           1957. 1957 |         66        2.05       29.06
           1958. 1958 |         43        1.33       30.39
           1959. 1959 |         61        1.89       32.29
           1960. 1960 |         63        1.96       34.24
           1961. 1961 |         57        1.77       36.01
           1962. 1962 |         47        1.46       37.47
           1963. 1963 |         62        1.92       39.40
           1964. 1964 |         52        1.61       41.01
           1965. 1965 |         80        2.48       43.50
           1966. 1966 |         56        1.74       45.23
           1967. 1967 |         78        2.42       47.66
           1968. 1968 |         57        1.77       49.43
           1969. 1969 |         56        1.74       51.16
           1970. 1970 |         62        1.92       53.09
           1971. 1971 |         53        1.65       54.73
           1972. 1972 |         49        1.52       56.26
           1973. 1973 |         34        1.06       57.31
           1974. 1974 |         42        1.30       58.62
           1975. 1975 |         52        1.61       60.23
           1976. 1976 |         42        1.30       61.53
           1977. 1977 |         69        2.14       63.68
           1978. 1978 |         60        1.86       65.54
           1979. 1979 |         55        1.71       67.25
           1980. 1980 |         57        1.77       69.02
           1981. 1981 |         65        2.02       71.03
           1982. 1982 |         69        2.14       73.18
           1983. 1983 |         60        1.86       75.04
           1984. 1984 |         61        1.89       76.93
           1985. 1985 |         77        2.39       79.32
           1986. 1986 |         61        1.89       81.22
           1987. 1987 |         46        1.43       82.65
           1988. 1988 |         66        2.05       84.69
           1989. 1989 |         53        1.65       86.34
           1990. 1990 |         41        1.27       87.61
           1991. 1991 |         62        1.92       89.54
           1992. 1992 |         67        2.08       91.62
           1993. 1993 |         64        1.99       93.60
           1994. 1994 |         58        1.80       95.41
           1995. 1995 |         48        1.49       96.90
           1996. 1996 |         21        0.65       97.55
           1997. 1997 |         18        0.56       98.11
           1998. 1998 |         14        0.43       98.54
           1999. 1999 |         13        0.40       98.94
           2000. 2000 |          7        0.22       99.16
           2001. 2001 |          3        0.09       99.25
           2002. 2002 |          4        0.12       99.38
                    . |         20        0.62      100.00
----------------------+-----------------------------------
                Total |      3,221      100.00

-> tabulation of stat2_gr3  

Alter gruppert |      Freq.     Percent        Cum.
---------------+-----------------------------------
1. 18-39 Jahre |        978       30.36       30.36
2. 40-59 Jahre |      1,120       34.77       65.14
 3. 60 Jahre + |      1,103       34.24       99.38
             . |         20        0.62      100.00
---------------+-----------------------------------
         Total |      3,221      100.00

. gen double age = 2020-stat2
(20 missing values generated)

. tab age, mis

        age |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |          4        0.12        0.12
         19 |          3        0.09        0.22
         20 |          7        0.22        0.43
         21 |         13        0.40        0.84
         22 |         14        0.43        1.27
         23 |         18        0.56        1.83
         24 |         21        0.65        2.48
         25 |         48        1.49        3.97
         26 |         58        1.80        5.77
         27 |         64        1.99        7.76
         28 |         67        2.08        9.84
         29 |         62        1.92       11.77
         30 |         41        1.27       13.04
         31 |         53        1.65       14.68
         32 |         66        2.05       16.73
         33 |         46        1.43       18.16
         34 |         61        1.89       20.06
         35 |         77        2.39       22.45
         36 |         61        1.89       24.34
         37 |         60        1.86       26.20
         38 |         69        2.14       28.35
         39 |         65        2.02       30.36
         40 |         57        1.77       32.13
         41 |         55        1.71       33.84
         42 |         60        1.86       35.70
         43 |         69        2.14       37.85
         44 |         42        1.30       39.15
         45 |         52        1.61       40.76
         46 |         42        1.30       42.07
         47 |         34        1.06       43.12
         48 |         49        1.52       44.64
         49 |         53        1.65       46.29
         50 |         62        1.92       48.21
         51 |         56        1.74       49.95
         52 |         57        1.77       51.72
         53 |         78        2.42       54.14
         54 |         56        1.74       55.88
         55 |         80        2.48       58.37
         56 |         52        1.61       59.98
         57 |         62        1.92       61.91
         58 |         47        1.46       63.37
         59 |         57        1.77       65.14
         60 |         63        1.96       67.09
         61 |         61        1.89       68.98
         62 |         43        1.33       70.32
         63 |         66        2.05       72.37
         64 |         53        1.65       74.01
         65 |         73        2.27       76.28
         66 |         86        2.67       78.95
         67 |         58        1.80       80.75
         68 |         63        1.96       82.71
         69 |         64        1.99       84.69
         70 |         61        1.89       86.59
         71 |         83        2.58       89.16
         72 |         53        1.65       90.81
         73 |         56        1.74       92.55
         74 |         29        0.90       93.45
         75 |         18        0.56       94.01
         76 |         26        0.81       94.82
         77 |         27        0.84       95.65
         78 |         21        0.65       96.31
         79 |         26        0.81       97.11
         80 |         24        0.75       97.86
         81 |         11        0.34       98.20
         82 |         14        0.43       98.63
         83 |         10        0.31       98.94
         84 |          2        0.06       99.01
         85 |          6        0.19       99.19
         86 |          1        0.03       99.22
         87 |          3        0.09       99.32
         89 |          1        0.03       99.35
         90 |          1        0.03       99.38
          . |         20        0.62      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. gen agedec = age/10
(20 missing values generated)

. 
. gen old = age>70

. tab old, mis

        old |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,789       86.59       86.59
          1 |        432       13.41      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Eastern Germany
. tab stat4 ost, mis //Berlin is grouped into Eastern, which we won't do

In welchem Bundesland |          Ostdeutschland
           leben Sie? | 0. Westde  1. Ostdeu          . |     Total
----------------------+---------------------------------+----------
 1. Baden-Württemberg |       321          0          0 |       321 
            2. Bayern |       383          0          0 |       383 
            3. Berlin |         0        282          0 |       282 
       4. Brandenburg |         0        184          0 |       184 
            5. Bremen |        31          0          0 |        31 
           6. Hamburg |        47          0          0 |        47 
            7. Hessen |       186          0          0 |       186 
8. Mecklenburg-Vorpom |         0        129          0 |       129 
     9. Niedersachsen |       220          0          0 |       220 
10. Nordrhein-Westfal |       517          0          0 |       517 
  11. Rheinland-Pfalz |       124          0          0 |       124 
         12. Saarland |        24          0          0 |        24 
          13. Sachsen |         0        323          0 |       323 
   14. Sachsen-Anhalt |         0        181          0 |       181 
15. Schleswig-Holstei |        92          0          0 |        92 
        16. Thüringen |         0        157          0 |       157 
                    . |         0          0         20 |        20 
----------------------+---------------------------------+----------
                Total |     1,945      1,256         20 |     3,221 

. gen float east=ost
(20 missing values generated)

. replace east=0 if stat4==3
(282 real changes made)

. tab stat4 east, mis

In welchem Bundesland |               east
           leben Sie? |         0          1          . |     Total
----------------------+---------------------------------+----------
 1. Baden-Württemberg |       321          0          0 |       321 
            2. Bayern |       383          0          0 |       383 
            3. Berlin |       282          0          0 |       282 
       4. Brandenburg |         0        184          0 |       184 
            5. Bremen |        31          0          0 |        31 
           6. Hamburg |        47          0          0 |        47 
            7. Hessen |       186          0          0 |       186 
8. Mecklenburg-Vorpom |         0        129          0 |       129 
     9. Niedersachsen |       220          0          0 |       220 
10. Nordrhein-Westfal |       517          0          0 |       517 
  11. Rheinland-Pfalz |       124          0          0 |       124 
         12. Saarland |        24          0          0 |        24 
          13. Sachsen |         0        323          0 |       323 
   14. Sachsen-Anhalt |         0        181          0 |       181 
15. Schleswig-Holstei |        92          0          0 |        92 
        16. Thüringen |         0        157          0 |       157 
                    . |         0          0         20 |        20 
----------------------+---------------------------------+----------
                Total |     2,227        974         20 |     3,221 

. tab east

       east |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,227       69.57       69.57
          1 |        974       30.43      100.00
------------+-----------------------------------
      Total |      3,201      100.00

. 
. tab stat7, mis

  In welchem Bundesland haben |
    Sie die meiste Zeit Ihrer |
          Kindheit verbracht? |      Freq.     Percent        Cum.
------------------------------+-----------------------------------
         1. Baden-Württemberg |        309        9.59        9.59
                    2. Bayern |        336       10.43       20.02
             3. Berlin (West) |        148        4.59       24.62
               4. Brandenburg |        156        4.84       29.46
                    5. Bremen |         26        0.81       30.27
                   6. Hamburg |         45        1.40       31.67
                    7. Hessen |        171        5.31       36.98
    8. Mecklenburg-Vorpommern |        124        3.85       40.83
             9. Niedersachsen |        213        6.61       47.44
      10. Nordrhein-Westfalen |        550       17.08       64.51
          11. Rheinland-Pfalz |        122        3.79       68.30
                 12. Saarland |         34        1.06       69.36
                  13. Sachsen |        339       10.52       79.88
           14. Sachsen-Anhalt |        207        6.43       86.31
       15. Schleswig-Holstein |         66        2.05       88.36
                16. Thüringen |        178        5.53       93.88
17. Außerhalb von Deutschland |         65        2.02       95.90
             18. Berlin (Ost) |         93        2.89       98.79
                            . |         39        1.21      100.00
------------------------------+-----------------------------------
                        Total |      3,221      100.00

. recast float stat7

. recode stat7 (1/3 5/7 9/12 15 17=0) (4 8 13 14 16 18 = 1) (.=.), gen(eastchild)
(3182 differences between stat7 and eastchild)

. label var eastchild ""

. tab stat7 eastchild, mis

In welchem Bundesland |
 haben Sie die meiste |
  Zeit Ihrer Kindheit |            eastchild
           verbracht? |         0          1          . |     Total
----------------------+---------------------------------+----------
 1. Baden-Württemberg |       309          0          0 |       309 
            2. Bayern |       336          0          0 |       336 
     3. Berlin (West) |       148          0          0 |       148 
       4. Brandenburg |         0        156          0 |       156 
            5. Bremen |        26          0          0 |        26 
           6. Hamburg |        45          0          0 |        45 
            7. Hessen |       171          0          0 |       171 
8. Mecklenburg-Vorpom |         0        124          0 |       124 
     9. Niedersachsen |       213          0          0 |       213 
10. Nordrhein-Westfal |       550          0          0 |       550 
  11. Rheinland-Pfalz |       122          0          0 |       122 
         12. Saarland |        34          0          0 |        34 
          13. Sachsen |         0        339          0 |       339 
   14. Sachsen-Anhalt |         0        207          0 |       207 
15. Schleswig-Holstei |        66          0          0 |        66 
        16. Thüringen |         0        178          0 |       178 
17. Außerhalb von Deu |        65          0          0 |        65 
     18. Berlin (Ost) |         0         93          0 |        93 
                    . |         0          0         39 |        39 
----------------------+---------------------------------+----------
                Total |     2,085      1,097         39 |     3,221 

. 
. *City vs. countryside
. *apparently n.a. in nov wave?
. gen float habitation = .
(3,221 missing values generated)

. 
. *Migration background
. tab1 stat3*, mis nol

-> tabulation of stat3a  

[Ich bin in |
Deutschland |
  geboren.] |
 Was trifft |
auf Sie zu? |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        135        4.19        4.19
          1 |      3,022       93.82       98.01
          8 |          7        0.22       98.23
          . |         57        1.77      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of stat3b  

     [Meine |
 Mutter ist |
         in |
Deutschland |
  geboren.] |
 Was trifft |
auf Sie zu? |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        285        8.85        8.85
          1 |      2,749       85.35       94.19
          8 |         16        0.50       94.69
          . |        171        5.31      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of stat3c  

[Mein Vater |
     ist in |
Deutschland |
  geboren.] |
 Was trifft |
auf Sie zu? |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        306        9.50        9.50
          1 |      2,702       83.89       93.39
          8 |         42        1.30       94.69
          . |        171        5.31      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. gen mig = 0 if (stat3a~=. & stat3b~=. & stat3c~=.)
(176 missing values generated)

. replace mig = 1 if (stat3a==0 | stat3b==0 | stat3c==0)
(395 real changes made)

. replace mig=. if (stat3a==8 | stat3b==8 | stat3c==8)
(46 real changes made, 46 to missing)

. tab mig, mis

        mig |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,612       81.09       81.09
          1 |        391       12.14       93.23
          . |        218        6.77      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Children
. tab stat12, mis

  Haben Sie |
    Kinder? |      Freq.     Percent        Cum.
------------+-----------------------------------
    0. Nein |      1,251       38.84       38.84
      1. Ja |      1,938       60.17       99.01
          . |         32        0.99      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. gen float children = stat12
(32 missing values generated)

. tab children, mis

   children |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,251       38.84       38.84
          1 |      1,938       60.17       99.01
          . |         32        0.99      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Children at home
. tab1 stat14, mis

-> tabulation of stat14  

 Und wie viele |
    davon sind |
  Kinder unter |
    16 Jahren? |      Freq.     Percent        Cum.
---------------+-----------------------------------
     0. keines |      1,711       53.12       53.12
          1. 1 |        306        9.50       62.62
          2. 2 |        225        6.99       69.61
          3. 3 |         43        1.33       70.94
          4. 4 |         16        0.50       71.44
5. 5 oder mehr |          3        0.09       71.53
             . |        917       28.47      100.00
---------------+-----------------------------------
         Total |      3,221      100.00

. gen float childrenathome = stat14>0 if stat14~=.
(917 missing values generated)

. replace childrenathome = 0 if stat13==1
(845 real changes made)

. tab childrenathome, mis

childrenath |
        ome |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,556       79.35       79.35
          1 |        593       18.41       97.76
          . |         72        2.24      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *General education in years (nur only general education)
. tab1 stat16*, mis

-> tabulation of stat16  

    Welchen höchsten allgemeinbildenden |
              Schulabschluss haben Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
             1. Noch in Schulausbildung |          8        0.25        0.25
2. Von der Schule abgegangen ohne Schul |         40        1.24        1.49
3. Haupt- oder Volksschulabschluss bzw. |        957       29.71       31.20
4. Mittlere Reife, Realschulabschluss b |      1,130       35.08       66.28
5. Fachhochschulreife (Abschluss einer  |        267        8.29       74.57
6. Abitur bzw. Erweiterte Oberschule mi |        799       24.81       99.38
                                      . |         20        0.62      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of stat16_gr3  

    Bildung gruppiert |      Freq.     Percent        Cum.
----------------------+-----------------------------------
1. Niedrigere Bildung |      1,005       31.20       31.20
  2. Mittlere Bildung |      1,130       35.08       66.28
    3. Höhere Bildung |      1,066       33.10       99.38
                    . |         20        0.62      100.00
----------------------+-----------------------------------
                Total |      3,221      100.00

. recast float stat16

. recode stat16 (1=.) (2=7) (3=9) (4=10) (5=12) (6=13), gen(educgenyr)
(3201 differences between stat16 and educgenyr)

. tab1 stat17* educgenyr, mis

-> tabulation of stat17  

           Welchen höchsten beruflichen |
        Ausbildungsabschluss haben Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Beruflich-betriebliche Anlernzeit mi |         61        1.89        1.89
           2. Teilfacharbeiterabschluss |         18        0.56        2.45
3. Abgeschlossene gewerbliche oder land |        549       17.04       19.50
  4. Abgeschlossene kaufmännische Lehre |        868       26.95       46.45
  5. Berufliches Praktikum, Volontariat |          7        0.22       46.66
            6. Berufsfachschulabschluss |        358       11.11       57.78
                  7. Fachschulabschluss |        130        4.04       61.81
8. Meister-, Techniker- oder gleichwert |        195        6.05       67.87
9. Fachhochschulabschluss (auch Abschlu |        214        6.64       74.51
                 10. Hochschulabschluss |        429       13.32       87.83
11. Keinen beruflichen Ausbildungsabsch |        210        6.52       94.35
                          12. Sonstiges |        145        4.50       98.85
                                      . |         37        1.15      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of stat17_other  

           [Sonstiges] Welchen höchsten |
 beruflichen Ausbildungsabschluss haben |
                                   Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                        |         37        1.15        1.15
                                .FILTER |      3,039       94.35       95.50
2 abgeschlossene Berufe, ein gewerbli.. |          1        0.03       95.53
                2 abgeschlossene Lehren |          1        0.03       95.56
    Abgeschlossene Gastronomiefachlehre |          1        0.03       95.59
            Abgeschlossene techn. Lehre |          1        0.03       95.62
Abgeschlossener Ausbildung zur Fachkr.. |          1        0.03       95.65
                      Altenpflegehelfer |          1        0.03       95.68
                         Altenpflegerin |          1        0.03       95.72
      Angeschlossene Ausbildung als zfa |          1        0.03       95.75
                           Arzthelferin |          1        0.03       95.78
                              Ausbilder |          1        0.03       95.81
                            Ausbilderin |          1        0.03       95.84
                             Ausbildung |          1        0.03       95.87
           Ausbildung in der Verwaltung |          1        0.03       95.90
                  Ausbildung mit Examen |          1        0.03       95.93
           Ausbildung zum Stabsoffizier |          1        0.03       95.96
       Ausbildung zum Stahlbauschlosser |          1        0.03       96.00
                                 Backer |          1        0.03       96.03
                           Bankfachwirt |          1        0.03       96.06
                        Beamtenlaufbahn |          1        0.03       96.09
                                Beamter |          1        0.03       96.12
                                Beamtin |          1        0.03       96.15
   Berufsausbildung im Gesundheitswesen |          1        0.03       96.18
                     Berufskraft Fahrer |          1        0.03       96.21
             Bin noch in der Ausbildung |          1        0.03       96.24
                         Binnerschiffer |          1        0.03       96.27
                           Buchbinderin |          1        0.03       96.31
                                BÃ€cker |          1        0.03       96.34
                              BÃ€ckerei |          1        0.03       96.37
              BÃ€ckereifachverkÃ€uferin |          1        0.03       96.40
                              BÃ€ckerin |          1        0.03       96.43
                     Derzeit noch dabei |          1        0.03       96.46
                                 Dreher |          2        0.06       96.52
                       Duale Ausbildung |          1        0.03       96.55
                            Erzieher*In |          1        0.03       96.58
                             Erzieherin |          3        0.09       96.68
Examinierte Gesundheits und Krankenpf.. |          1        0.03       96.71
           Examinierte Krankenschwester |          1        0.03       96.74
                           Facharbeiter |          5        0.16       96.90
          Facharbeiter Krankenschwester |          1        0.03       96.93
                      Facharbeiterbrief |          1        0.03       96.96
                  Facharztweiterbildung |          1        0.03       96.99
                       Fachschulstudium |          1        0.03       97.02
  FachverkÃ€uferin Lebensmittelhandwerk |          1        0.03       97.05
                               Fachwirt |          3        0.09       97.14
                             Fahrlehrer |          1        0.03       97.17
      Feinmechaniker, Rettungsassistent |          1        0.03       97.21
                       Fernmeldetechnik |          1        0.03       97.24
            FleischereifachverkÃ€uferin |          1        0.03       97.27
                        Friseurhandwerk |          1        0.03       97.30
                           Friseurlehre |          1        0.03       97.33
                         Gastrofachfrau |          1        0.03       97.36
                            Gastronomie |          1        0.03       97.39
                                Geselle |          1        0.03       97.42
                          Goldschmiedin |          1        0.03       97.45
                             GÃ€rtnerin |          1        0.03       97.49
                         Hauswirtschaft |          1        0.03       97.52
                Hauswittschaftshelferin |          1        0.03       97.55
                  Heilerziehungspfleger |          2        0.06       97.61
 Kaufm. Lehre wg. Flucht ohne AbschluÃ |          1        0.03       97.64
                                Kellner |          1        0.03       97.67
Kfz-Schlosser, FachverkÃ€ufer, Versic.. |          1        0.03       97.70
                       Krankenschwester |          2        0.06       97.76
                 Krankenschwesterexamen |          1        0.03       97.80
                     Kriminalakademisch |          1        0.03       97.83
                                KÃ¶chin |          1        0.03       97.86
LEHRE ALS KONDITOREI FACHVERKÃUFER M.. |          1        0.03       97.89
           Laborantin/kaufm. Ausbildung |          1        0.03       97.92
                     Laufbahnausbildung |          1        0.03       97.95
              Lehre im Gesundheitswesen |          1        0.03       97.98
        Lehre mit Facharbeiterabschluss |          1        0.03       98.01
                             LokfÃŒhrer |          1        0.03       98.04
                                    MFA |          1        0.03       98.08
                                    MTA |          1        0.03       98.11
                                 Maurer |          2        0.06       98.17
                 Mechaniker/Kassiererin |          1        0.03       98.20
           Medizinische fachangestellte |          1        0.03       98.23
                               NÃ€herin |          1        0.03       98.26
                          Offsetdrucker |          1        0.03       98.29
                            Opernschule |          1        0.03       98.32
                                    PTA |          1        0.03       98.35
                            Papiemacher |          1        0.03       98.39
                      Physiotherapeutin |          1        0.03       98.42
                         Polizeibeamter |          2        0.06       98.48
                            Postbeamtin |          1        0.03       98.51
                       Postfacharbeiter |          1        0.03       98.54
                             PÃ€dagogik |          1        0.03       98.57
                    ReNoFachangestellte |          1        0.03       98.60
           Rechtsanwaltsfachangestellte |          1        0.03       98.63
        SachverstÃ€ndiger im Baugewerbe |          1        0.03       98.67
                            Schneiderin |          1        0.03       98.70
                          Siemensschule |          1        0.03       98.73
Sowie 2. abgeschlossene Ausbildung vo.. |          1        0.03       98.76
                     Sozialversicherung |          1        0.03       98.79
     Sozialversicherungsfachangestellte |          1        0.03       98.82
             Staatl. gepr. Betriebswirt |          1        0.03       98.85
                           Staatsexamen |          2        0.06       98.91
        Stattlich anerkannte Erzieherin |          1        0.03       98.94
                              Studentin |          1        0.03       98.98
                                Studium |          1        0.03       99.01
                  Technische Zeichnerin |          1        0.03       99.04
                    Tennistrainerschein |          1        0.03       99.07
                               Tischler |          1        0.03       99.10
                           UniversitÃ€t |          1        0.03       99.13
                           VerkÃ€uferin |          1        0.03       99.16
                             Verwaltung |          1        0.03       99.19
             Verwaltungsfachangestellte |          1        0.03       99.22
            Verwaltungsfachangestellter |          1        0.03       99.25
                                 Vhemie |          1        0.03       99.29
Waldorf kindergÃ€rtner ohne staatlich.. |          1        0.03       99.32
              Weiterbildung lagerhelfer |          1        0.03       99.35
                                    ZMA |          1        0.03       99.38
                       Zahnarzthelferin |          2        0.06       99.44
       Zahnmedizinische Fachangestellte |          1        0.03       99.47
aUSBILDUNG IM MITTLEREN bEAMTEMLAUFBA.. |          1        0.03       99.50
         abgeschlossene Handwerkerlehre |          1        0.03       99.53
abgeschlossene Lehre Notarfachangeste.. |          1        0.03       99.57
            abgeschlossene techn. Lehre |          1        0.03       99.60
                  ausgebildeter Beamter |          1        0.03       99.63
berufsausbildung mit abgeschlossener .. |          1        0.03       99.66
                                  db ag |          1        0.03       99.69
            examiniert Berufsausbildung |          1        0.03       99.72
                           fahclagerist |          1        0.03       99.75
industriemechanikerin maschinen syste.. |          1        0.03       99.78
                               kapitÃ€n |          1        0.03       99.81
                            keine Lehre |          1        0.03       99.84
                  krankenpflegehelferin |          1        0.03       99.88
                     noch in Ausbildung |          1        0.03       99.91
                              schreiner |          1        0.03       99.94
                   Ãffentlicher Dienst |          2        0.06      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of stat17p  

           Welchen höchsten beruflichen |
           Ausbildungsabschluss hat Ihr |
                 Partner/Ihre Partnerin |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Beruflich-betriebliche Anlernzeit mi |         41        1.27        1.27
           2. Teilfacharbeiterabschluss |         27        0.84        2.11
3. Abgeschlossene gewerbliche oder land |        472       14.65       16.76
  4. Abgeschlossene kaufmännische Lehre |        382       11.86       28.62
  5. Berufliches Praktikum, Volontariat |          4        0.12       28.75
            6. Berufsfachschulabschluss |        237        7.36       36.11
                  7. Fachschulabschluss |        119        3.69       39.80
8. Meister-, Techniker- oder gleichwert |        199        6.18       45.98
9. Fachhochschulabschluss (auch Abschlu |        106        3.29       49.27
                 10. Hochschulabschluss |        270        8.38       57.65
11. Mein Partner hat keinen (oder noch  |        127        3.94       61.60
                          12. Sonstiges |         81        2.51       64.11
                                      . |      1,156       35.89      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of stat17p_other  

           [Sonstiges] Welchen höchsten |
   beruflichen Ausbildungsabschluss hat |
                         Ihr Partner/Ih |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                        |      1,156       35.89       35.89
                                .FILTER |      1,984       61.60       97.49
Abgeschlossene Ausbildung als KFZ Mec.. |          1        0.03       97.52
                         Artz helfenrin |          1        0.03       97.55
                           Arzthelferin |          3        0.09       97.64
                        Automatendreher |          1        0.03       97.67
    Bank und Finnanzwessen UniversitÃ€t |          1        0.03       97.70
                            Bauzeichner |          1        0.03       97.73
                                Beamter |          3        0.09       97.83
                                Beamtin |          2        0.06       97.89
                                    Bwl |          1        0.03       97.92
                                BÃ cker |          1        0.03       97.95
                                BÃ€cker |          1        0.03       97.98
                 BÃ€cker und Buchhalter |          1        0.03       98.01
                                 Chemie |          1        0.03       98.04
                              Chemikant |          1        0.03       98.08
                             Dachdecker |          1        0.03       98.11
                        Diplom-Designer |          1        0.03       98.14
                               Erzieher |          1        0.03       98.17
                             Erzieherin |          1        0.03       98.20
                                 Examen |          1        0.03       98.23
                          Examen Pflege |          1        0.03       98.26
                           Facharbeiter |          3        0.09       98.35
                   Fachkrankenschwester |          1        0.03       98.39
                Gas-Wasser-Installateur |          1        0.03       98.42
                            Gastronomie |          1        0.03       98.45
                    Gesellenausbildnung |          1        0.03       98.48
                          Gesellenbrief |          1        0.03       98.51
        Gesundheits- und Krankenpfleger |          1        0.03       98.54
                 GlÃ€ser und Stukkateur |          1        0.03       98.57
             Grafik Designer; KÃŒnstler |          1        0.03       98.60
                          Handelsschule |          1        0.03       98.63
                   Handwerkerausbildung |          1        0.03       98.67
                              It System |          1        0.03       98.70
                         Justizfachwirt |          1        0.03       98.73
                 Koch, Bilanzbuchhalter |          1        0.03       98.76
                                KÃ¶chin |          1        0.03       98.79
                   LOKBetriebsinspektor |          1        0.03       98.82
                          Landesbeamter |          1        0.03       98.85
                                  Lehre |          1        0.03       98.88
        Lehre mit Facharbeiterabschluss |          1        0.03       98.91
                                  Maler |          1        0.03       98.94
                       MaschinenfÃŒhrer |          1        0.03       98.98
                                Metzger |          1        0.03       99.01
                     Noch in Ausbildung |          1        0.03       99.04
                           OpernsÃ€nger |          1        0.03       99.07
                             Pferdewirt |          1        0.03       99.10
                         Polizeibeamter |          1        0.03       99.13
                                 Presse |          1        0.03       99.16
                         Raumausstatter |          1        0.03       99.19
                              Schlosser |          1        0.03       99.22
                            Schneiderin |          1        0.03       99.25
                              Schreiner |          1        0.03       99.29
         Schreiner und zimmermannslehre |          1        0.03       99.32
     Sozialversicherungsfachangestellte |          1        0.03       99.35
                               Studiert |          1        0.03       99.38
                         Studium Master |          1        0.03       99.41
                 Technischer Zeichnerin |          1        0.03       99.44
                    Tennistrainerschein |          1        0.03       99.47
                    Textielfacharbeiter |          1        0.03       99.50
                       Zahnarzthelferin |          1        0.03       99.53
         abgeschlossene Handwerkerlehre |          1        0.03       99.57
           abgeschlossene IT-Ausbildung |          1        0.03       99.60
                            altenpflege |          1        0.03       99.63
                   ausgebiltete Beamtin |          1        0.03       99.66
             examinierte Altenpflegerin |          1        0.03       99.69
                          kaufmÃ€nnisch |          1        0.03       99.72
                       krankenschwester |          1        0.03       99.75
                    maler und lackierer |          1        0.03       99.78
          rechtsanwalts und notagehilfe |          1        0.03       99.81
                              schlosser |          1        0.03       99.84
            staatl.anerkannter Podologe |          1        0.03       99.88
                     staatliches Examen |          1        0.03       99.91
                                     xy |          1        0.03       99.94
                   Ãffentlicher Dienst |          2        0.06      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of educgenyr  

  RECODE of |
     stat16 |
   (Welchen |
   höchsten |
allgemeinbi |
    ldenden |
Schulabschl |
  uss haben |
      Sie?) |      Freq.     Percent        Cum.
------------+-----------------------------------
          7 |         40        1.24        1.24
          9 |        957       29.71       30.95
         10 |      1,130       35.08       66.04
         12 |        267        8.29       74.32
         13 |        799       24.81       99.13
          . |         28        0.87      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *General education categorical
. gen float educgencat = stat16_gr3 
(20 missing values generated)

. tab educgencat, mis

 educgencat |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,005       31.20       31.20
          2 |      1,130       35.08       66.28
          3 |      1,066       33.10       99.38
          . |         20        0.62      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. tab educgencat stat16, mis

           |        Welchen höchsten allgemeinbildenden Schulabschluss haben Sie?
educgencat | 1. Noch i  2. Von de  3. Haupt-  4. Mittle  5. Fachho  6. Abitur          . |     Total
-----------+-----------------------------------------------------------------------------+----------
         1 |         8         40        957          0          0          0          0 |     1,005 
         2 |         0          0          0      1,130          0          0          0 |     1,130 
         3 |         0          0          0          0        267        799          0 |     1,066 
         . |         0          0          0          0          0          0         20 |        20 
-----------+-----------------------------------------------------------------------------+----------
     Total |         8         40        957      1,130        267        799         20 |     3,221 

. 
. *Vocational education in years
. tab1 stat17 stat17_other, mis

-> tabulation of stat17  

           Welchen höchsten beruflichen |
        Ausbildungsabschluss haben Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Beruflich-betriebliche Anlernzeit mi |         61        1.89        1.89
           2. Teilfacharbeiterabschluss |         18        0.56        2.45
3. Abgeschlossene gewerbliche oder land |        549       17.04       19.50
  4. Abgeschlossene kaufmännische Lehre |        868       26.95       46.45
  5. Berufliches Praktikum, Volontariat |          7        0.22       46.66
            6. Berufsfachschulabschluss |        358       11.11       57.78
                  7. Fachschulabschluss |        130        4.04       61.81
8. Meister-, Techniker- oder gleichwert |        195        6.05       67.87
9. Fachhochschulabschluss (auch Abschlu |        214        6.64       74.51
                 10. Hochschulabschluss |        429       13.32       87.83
11. Keinen beruflichen Ausbildungsabsch |        210        6.52       94.35
                          12. Sonstiges |        145        4.50       98.85
                                      . |         37        1.15      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of stat17_other  

           [Sonstiges] Welchen höchsten |
 beruflichen Ausbildungsabschluss haben |
                                   Sie? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                        |         37        1.15        1.15
                                .FILTER |      3,039       94.35       95.50
2 abgeschlossene Berufe, ein gewerbli.. |          1        0.03       95.53
                2 abgeschlossene Lehren |          1        0.03       95.56
    Abgeschlossene Gastronomiefachlehre |          1        0.03       95.59
            Abgeschlossene techn. Lehre |          1        0.03       95.62
Abgeschlossener Ausbildung zur Fachkr.. |          1        0.03       95.65
                      Altenpflegehelfer |          1        0.03       95.68
                         Altenpflegerin |          1        0.03       95.72
      Angeschlossene Ausbildung als zfa |          1        0.03       95.75
                           Arzthelferin |          1        0.03       95.78
                              Ausbilder |          1        0.03       95.81
                            Ausbilderin |          1        0.03       95.84
                             Ausbildung |          1        0.03       95.87
           Ausbildung in der Verwaltung |          1        0.03       95.90
                  Ausbildung mit Examen |          1        0.03       95.93
           Ausbildung zum Stabsoffizier |          1        0.03       95.96
       Ausbildung zum Stahlbauschlosser |          1        0.03       96.00
                                 Backer |          1        0.03       96.03
                           Bankfachwirt |          1        0.03       96.06
                        Beamtenlaufbahn |          1        0.03       96.09
                                Beamter |          1        0.03       96.12
                                Beamtin |          1        0.03       96.15
   Berufsausbildung im Gesundheitswesen |          1        0.03       96.18
                     Berufskraft Fahrer |          1        0.03       96.21
             Bin noch in der Ausbildung |          1        0.03       96.24
                         Binnerschiffer |          1        0.03       96.27
                           Buchbinderin |          1        0.03       96.31
                                BÃ€cker |          1        0.03       96.34
                              BÃ€ckerei |          1        0.03       96.37
              BÃ€ckereifachverkÃ€uferin |          1        0.03       96.40
                              BÃ€ckerin |          1        0.03       96.43
                     Derzeit noch dabei |          1        0.03       96.46
                                 Dreher |          2        0.06       96.52
                       Duale Ausbildung |          1        0.03       96.55
                            Erzieher*In |          1        0.03       96.58
                             Erzieherin |          3        0.09       96.68
Examinierte Gesundheits und Krankenpf.. |          1        0.03       96.71
           Examinierte Krankenschwester |          1        0.03       96.74
                           Facharbeiter |          5        0.16       96.90
          Facharbeiter Krankenschwester |          1        0.03       96.93
                      Facharbeiterbrief |          1        0.03       96.96
                  Facharztweiterbildung |          1        0.03       96.99
                       Fachschulstudium |          1        0.03       97.02
  FachverkÃ€uferin Lebensmittelhandwerk |          1        0.03       97.05
                               Fachwirt |          3        0.09       97.14
                             Fahrlehrer |          1        0.03       97.17
      Feinmechaniker, Rettungsassistent |          1        0.03       97.21
                       Fernmeldetechnik |          1        0.03       97.24
            FleischereifachverkÃ€uferin |          1        0.03       97.27
                        Friseurhandwerk |          1        0.03       97.30
                           Friseurlehre |          1        0.03       97.33
                         Gastrofachfrau |          1        0.03       97.36
                            Gastronomie |          1        0.03       97.39
                                Geselle |          1        0.03       97.42
                          Goldschmiedin |          1        0.03       97.45
                             GÃ€rtnerin |          1        0.03       97.49
                         Hauswirtschaft |          1        0.03       97.52
                Hauswittschaftshelferin |          1        0.03       97.55
                  Heilerziehungspfleger |          2        0.06       97.61
 Kaufm. Lehre wg. Flucht ohne AbschluÃ |          1        0.03       97.64
                                Kellner |          1        0.03       97.67
Kfz-Schlosser, FachverkÃ€ufer, Versic.. |          1        0.03       97.70
                       Krankenschwester |          2        0.06       97.76
                 Krankenschwesterexamen |          1        0.03       97.80
                     Kriminalakademisch |          1        0.03       97.83
                                KÃ¶chin |          1        0.03       97.86
LEHRE ALS KONDITOREI FACHVERKÃUFER M.. |          1        0.03       97.89
           Laborantin/kaufm. Ausbildung |          1        0.03       97.92
                     Laufbahnausbildung |          1        0.03       97.95
              Lehre im Gesundheitswesen |          1        0.03       97.98
        Lehre mit Facharbeiterabschluss |          1        0.03       98.01
                             LokfÃŒhrer |          1        0.03       98.04
                                    MFA |          1        0.03       98.08
                                    MTA |          1        0.03       98.11
                                 Maurer |          2        0.06       98.17
                 Mechaniker/Kassiererin |          1        0.03       98.20
           Medizinische fachangestellte |          1        0.03       98.23
                               NÃ€herin |          1        0.03       98.26
                          Offsetdrucker |          1        0.03       98.29
                            Opernschule |          1        0.03       98.32
                                    PTA |          1        0.03       98.35
                            Papiemacher |          1        0.03       98.39
                      Physiotherapeutin |          1        0.03       98.42
                         Polizeibeamter |          2        0.06       98.48
                            Postbeamtin |          1        0.03       98.51
                       Postfacharbeiter |          1        0.03       98.54
                             PÃ€dagogik |          1        0.03       98.57
                    ReNoFachangestellte |          1        0.03       98.60
           Rechtsanwaltsfachangestellte |          1        0.03       98.63
        SachverstÃ€ndiger im Baugewerbe |          1        0.03       98.67
                            Schneiderin |          1        0.03       98.70
                          Siemensschule |          1        0.03       98.73
Sowie 2. abgeschlossene Ausbildung vo.. |          1        0.03       98.76
                     Sozialversicherung |          1        0.03       98.79
     Sozialversicherungsfachangestellte |          1        0.03       98.82
             Staatl. gepr. Betriebswirt |          1        0.03       98.85
                           Staatsexamen |          2        0.06       98.91
        Stattlich anerkannte Erzieherin |          1        0.03       98.94
                              Studentin |          1        0.03       98.98
                                Studium |          1        0.03       99.01
                  Technische Zeichnerin |          1        0.03       99.04
                    Tennistrainerschein |          1        0.03       99.07
                               Tischler |          1        0.03       99.10
                           UniversitÃ€t |          1        0.03       99.13
                           VerkÃ€uferin |          1        0.03       99.16
                             Verwaltung |          1        0.03       99.19
             Verwaltungsfachangestellte |          1        0.03       99.22
            Verwaltungsfachangestellter |          1        0.03       99.25
                                 Vhemie |          1        0.03       99.29
Waldorf kindergÃ€rtner ohne staatlich.. |          1        0.03       99.32
              Weiterbildung lagerhelfer |          1        0.03       99.35
                                    ZMA |          1        0.03       99.38
                       Zahnarzthelferin |          2        0.06       99.44
       Zahnmedizinische Fachangestellte |          1        0.03       99.47
aUSBILDUNG IM MITTLEREN bEAMTEMLAUFBA.. |          1        0.03       99.50
         abgeschlossene Handwerkerlehre |          1        0.03       99.53
abgeschlossene Lehre Notarfachangeste.. |          1        0.03       99.57
            abgeschlossene techn. Lehre |          1        0.03       99.60
                  ausgebildeter Beamter |          1        0.03       99.63
berufsausbildung mit abgeschlossener .. |          1        0.03       99.66
                                  db ag |          1        0.03       99.69
            examiniert Berufsausbildung |          1        0.03       99.72
                           fahclagerist |          1        0.03       99.75
industriemechanikerin maschinen syste.. |          1        0.03       99.78
                               kapitÃ€n |          1        0.03       99.81
                            keine Lehre |          1        0.03       99.84
                  krankenpflegehelferin |          1        0.03       99.88
                     noch in Ausbildung |          1        0.03       99.91
                              schreiner |          1        0.03       99.94
                   Ãffentlicher Dienst |          2        0.06      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

. recast float stat17

. recode stat17 (1=1) (2=1) (3=3) (4=3) (5=0.5) (6=3) (7=3) (8=4) (9=4) (10=3) (11=0) (12=2), gen(educvocyr)
(2574 differences between stat17 and educvocyr)

. tab educvocyr , mis

  RECODE of |
     stat17 |
   (Welchen |
   höchsten |
beruflichen |
Ausbildungs |
  abschluss |
haben Sie?) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        210        6.52        6.52
         .5 |          7        0.22        6.74
          1 |         79        2.45        9.19
          2 |        145        4.50       13.69
          3 |      2,334       72.46       86.15
          4 |        409       12.70       98.85
          . |         37        1.15      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. tab stat18, mis

                   Um welche Art von |
(Fach-)Hochschulabschluss handelt es |
           sich dabei? Bitte geben S |      Freq.     Percent        Cum.
-------------------------------------+-----------------------------------
                         1. Bachelor |        120        3.73        3.73
                           2. Master |         76        2.36        6.09
                           3. Diplom |        288        8.94       15.03
                         4. Magister |         14        0.43       15.46
5. Staatsexamen oder Lehramtsprüfung |         82        2.55       18.01
                        6. Promotion |         32        0.99       19.00
              7. Sonstiger Abschluss |         30        0.93       19.93
                                   . |      2,579       80.07      100.00
-------------------------------------+-----------------------------------
                               Total |      3,221      100.00

. recast float stat18

. recode stat18 (1=0) (2/5=2) (6=6) (7=1), gen(univyr)
(534 differences between stat18 and univyr)

. tab stat17 univyr, mis

     Welchen höchsten |
          beruflichen |          RECODE of stat18 (Um welche Art von
 Ausbildungsabschluss |      (Fach-)Hochschulabschluss handelt es sich da
           haben Sie? |         0          1          2          6          . |     Total
----------------------+-------------------------------------------------------+----------
1. Beruflich-betriebl |         0          0          0          0         61 |        61 
2. Teilfacharbeiterab |         0          0          0          0         18 |        18 
3. Abgeschlossene gew |         0          0          0          0        549 |       549 
4. Abgeschlossene kau |         0          0          0          0        868 |       868 
5. Berufliches Prakti |         0          0          0          0          7 |         7 
6. Berufsfachschulabs |         0          0          0          0        358 |       358 
7. Fachschulabschluss |         0          0          0          0        130 |       130 
8. Meister-, Technike |         0          0          0          0        195 |       195 
9. Fachhochschulabsch |        36         23        155          0          0 |       214 
10. Hochschulabschlus |        84          7        305         32          1 |       429 
11. Keinen berufliche |         0          0          0          0        210 |       210 
        12. Sonstiges |         0          0          0          0        145 |       145 
                    . |         0          0          0          0         37 |        37 
----------------------+-------------------------------------------------------+----------
                Total |       120         30        460         32      2,579 |     3,221 

. replace educvocyr = educvocyr+univyr if univyr<.
(522 real changes made)

. tab educvocyr, mis

  RECODE of |
     stat17 |
   (Welchen |
   höchsten |
beruflichen |
Ausbildungs |
  abschluss |
haben Sie?) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        210        6.52        6.52
         .5 |          7        0.22        6.74
          1 |         79        2.45        9.19
          2 |        145        4.50       13.69
          3 |      1,990       61.78       75.47
          4 |        238        7.39       82.86
          5 |        328       10.18       93.05
          6 |        155        4.81       97.86
          9 |         32        0.99       98.85
          . |         37        1.15      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. drop univyr

. 
. *All education in years
. gen educyr = educgenyr+educvocyr
(37 missing values generated)

. tab educyr, mis

     educyr |      Freq.     Percent        Cum.
------------+-----------------------------------
          7 |         24        0.75        0.75
        7.5 |          1        0.03        0.78
          8 |          2        0.06        0.84
          9 |        102        3.17        4.00
        9.5 |          3        0.09        4.10
         10 |         99        3.07        7.17
         11 |         70        2.17        9.34
         12 |        768       23.84       33.19
         13 |        987       30.64       63.83
       13.5 |          3        0.09       63.92
         14 |        103        3.20       67.12
         15 |        164        5.09       72.21
         16 |        311        9.66       81.87
         17 |         83        2.58       84.45
         18 |        337       10.46       94.91
         19 |         95        2.95       97.86
         22 |         32        0.99       98.85
          . |         37        1.15      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Unemployed
. tab1 erw1 erw15h, mis

-> tabulation of erw1  

            Welche Beschäftigung trifft |
                überwiegend auf Sie zu? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Vollzeit erwerbstätig (mind. 35h/Woc |      1,469       45.61       45.61
2. Teilzeit erwerbstätig (15h bis unter |        377       11.70       57.31
3. Geringfügig erwerbstätig (weniger al |         74        2.30       59.61
            4. Elternzeit, Mutterschutz |         37        1.15       60.76
                     5. Auszubildende/r |         25        0.78       61.53
              6. Schüler/in, Student/in |         66        2.05       63.58
7. Sozialer Freiwilligendienst, BFD, FS |          1        0.03       63.61
                          8. Arbeitslos |         91        2.83       66.44
                  9. Hausfrau, Hausmann |         79        2.45       68.89
           10. Rentner/in, Pensionär/in |        935       29.03       97.92
                          11. Sonstiges |         47        1.46       99.38
                                      . |         20        0.62      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of erw15h  

Erwerbssituation |
     im Moment - |
      Arbeitslos |      Freq.     Percent        Cum.
-----------------+-----------------------------------
0. Nicht Gewählt |      1,609       49.95       49.95
           1. Ja |          6        0.19       50.14
               . |      1,606       49.86      100.00
-----------------+-----------------------------------
           Total |      3,221      100.00

. gen float unemployed = erw1==8

. replace unemployed = 1 if erw15h==1
(6 real changes made)

. tab unemployed, mis

 unemployed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,124       96.99       96.99
          1 |         97        3.01      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Partner
. tab1 stat8 stat9, mis

-> tabulation of stat8  

             Wie ist Ihr Familienstand? |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Verheiratet, mit Ehepartner/in zusam |      1,622       50.36       50.36
2. Eingetragene Partnerschaft (zusammen |        101        3.14       53.49
3. Verheiratet, dauerhaft getrennt lebe |         59        1.83       55.32
4. Eingetragene Partnerschaft (getrennt |         15        0.47       55.79
          5. Ledig, war nie verheiratet |        909       28.22       84.01
6. Geschieden / eingetragene Partnersch |        311        9.66       93.67
7. Verwitwet / Lebenspartner/in aus ein |        184        5.71       99.38
                                      . |         20        0.62      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of stat9  

  Haben Sie |
    derzeit |
 eine feste |
Partnerscha |
        ft? |      Freq.     Percent        Cum.
------------+-----------------------------------
    0. Nein |        859       26.67       26.67
      1. Ja |        619       19.22       45.89
          . |      1,743       54.11      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. gen float partner = (stat8==1 | stat8==2)

. tab partner

    partner |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,498       46.51       46.51
          1 |      1,723       53.49      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. replace partner=1 if stat9==1
(619 real changes made)

. tab partner, mis

    partner |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        879       27.29       27.29
          1 |      2,342       72.71      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Weight
. gen float persweight = gewicht
(20 missing values generated)

. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Variables appearing only in November wave
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Identification with Eastern Germany
. tab ost_verb, mis

            Fühlen Sie sich im |
   Allgemeinen als Deutsche(r) |
  oder mehr als Ostdeutsche(r) |
                          oder |      Freq.     Percent        Cum.
-------------------------------+-----------------------------------
       1. Eher als Deutsche(r) |      2,119       65.79       65.79
    2. Eher als Ostdeutsche(r) |        553       17.17       82.96
   3. Eher als Westdeutsche(r) |        374       11.61       94.57
              4. Unentschieden |         91        2.83       97.39
5. Ich bin kein(e) Deutsche(r) |         59        1.83       99.22
                             . |         25        0.78      100.00
-------------------------------+-----------------------------------
                         Total |      3,221      100.00

. recast float ost_verb

. recode ost_verb (4/5=4), gen(eastaffil)
(59 differences between ost_verb and eastaffil)

. label define eastaffil 1 "Deutscher" 2 "Ostdeutscher" 3 "Westdeutscher" 4 "unentsch./kein D"

. label value eastaffil eastaffil

. tab eastaffil, mis

       RECODE of |
        ost_verb |
     (Fühlen Sie |
         sich im |
 Allgemeinen als |
Deutsche(r) oder |
        mehr als |      Freq.     Percent        Cum.
-----------------+-----------------------------------
       Deutscher |      2,119       65.79       65.79
    Ostdeutscher |        553       17.17       82.96
   Westdeutscher |        374       11.61       94.57
unentsch./kein D |        150        4.66       99.22
               . |         25        0.78      100.00
-----------------+-----------------------------------
           Total |      3,221      100.00

. 
. gen fullossi = eastaffil==2 if eastaffil~=.
(25 missing values generated)

. tab fullossi, mis

   fullossi |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,643       82.06       82.06
          1 |        553       17.17       99.22
          . |         25        0.78      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Feeling of being lagged behind
. tab zufr4, mis

         Empfindung: Allen in |
  Deutschland geht es besser, |
       aber ich bleibe zurück |      Freq.     Percent        Cum.
------------------------------+-----------------------------------
 1. Ich empfinde das auch so. |        533       16.55       16.55
2. Ich empfinde das nicht so. |      2,108       65.45       81.99
             3. Unentschieden |        556       17.26       99.25
                            . |         24        0.75      100.00
------------------------------+-----------------------------------
                        Total |      3,221      100.00

. gen float lagbehind = zufr4==1 if zufr4~=.
(24 missing values generated)

. tab lagbehind, mis

  lagbehind |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,664       82.71       82.71
          1 |        533       16.55       99.25
          . |         24        0.75      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. *Conspiracy and other opinions towards Corona
. tab1 mein1* mein2*, mis

-> tabulation of mein1a  

 Aussagen zutreffen: Coronavirus Labor, |
                      biologische Waffe |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Kann ich mir überhaupt nicht vorstel |        817       25.36       25.36
  2. Kann ich mir eher nicht vorstellen |        693       21.52       46.88
                       3. Unentschieden |        505       15.68       62.56
       4. Kann ich mir schon vorstellen |        835       25.92       88.48
    5. Kann ich mir sehr gut vorstellen |        342       10.62       99.10
                                      . |         29        0.90      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of mein1b  

        Aussagen zutreffen: Bevölkerung |
                           zwangsimpfen |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Kann ich mir überhaupt nicht vorstel |      1,402       43.53       43.53
  2. Kann ich mir eher nicht vorstellen |        751       23.32       66.84
                       3. Unentschieden |        419       13.01       79.85
       4. Kann ich mir schon vorstellen |        412       12.79       92.64
    5. Kann ich mir sehr gut vorstellen |        204        6.33       98.98
                                      . |         33        1.02      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of mein1c  

      Aussagen zutreffen: Eliten wollen |
                Öffentlichkeit täuschen |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1. Kann ich mir überhaupt nicht vorstel |      1,009       31.33       31.33
  2. Kann ich mir eher nicht vorstellen |        716       22.23       53.55
                       3. Unentschieden |        526       16.33       69.89
       4. Kann ich mir schon vorstellen |        616       19.12       89.01
    5. Kann ich mir sehr gut vorstellen |        322       10.00       99.01
                                      . |         32        0.99      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,221      100.00

-> tabulation of mein2a  

         Aussagen zustimmen: |
    Alternative Heilmethoden |
               helfen besser |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
1. Stimme überhaupt nicht zu |        637       19.78       19.78
     2. Stimme eher nicht zu |      1,056       32.78       52.56
            3. Unentschieden |      1,184       36.76       89.32
           4. Stimme eher zu |        250        7.76       97.08
  5. Stimme voll und ganz zu |         70        2.17       99.25
                           . |         24        0.75      100.00
-----------------------------+-----------------------------------
                       Total |      3,221      100.00

-> tabulation of mein2b  

  Aussagen zustimmen: Corona |
  nicht schlimmer als Grippe |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
1. Stimme überhaupt nicht zu |      1,058       32.85       32.85
     2. Stimme eher nicht zu |        917       28.47       61.32
            3. Unentschieden |        554       17.20       78.52
           4. Stimme eher zu |        459       14.25       92.77
  5. Stimme voll und ganz zu |        209        6.49       99.25
                           . |         24        0.75      100.00
-----------------------------+-----------------------------------
                       Total |      3,221      100.00

-> tabulation of mein2c  

Aussagen zustimmen: Experten |
         glauben, dass Virus |
              gefährlich ist |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
1. Stimme überhaupt nicht zu |        163        5.06        5.06
     2. Stimme eher nicht zu |        274        8.51       13.57
            3. Unentschieden |        510       15.83       29.40
           4. Stimme eher zu |      1,123       34.86       64.27
  5. Stimme voll und ganz zu |      1,125       34.93       99.19
                           . |         26        0.81      100.00
-----------------------------+-----------------------------------
                       Total |      3,221      100.00

-> tabulation of mein2d  

         Aussagen zustimmen: |
        Staatliche Maßnahmen |
                 übertrieben |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
1. Stimme überhaupt nicht zu |        867       26.92       26.92
     2. Stimme eher nicht zu |        970       30.11       57.03
            3. Unentschieden |        561       17.42       74.45
           4. Stimme eher zu |        476       14.78       89.23
  5. Stimme voll und ganz zu |        321        9.97       99.19
                           . |         26        0.81      100.00
-----------------------------+-----------------------------------
                       Total |      3,221      100.00

. foreach x of varlist mein1* mein2* {
  2.     recast double `x'
  3. }

. 
. factor mein1* mein2* [aweight=gewicht], pcf blanks(.4) mineigen(.9)
(sum of wgt is 3,156.025)
(obs=3,166)

Factor analysis/correlation                      Number of obs    =      3,166
    Method: principal-component factors          Retained factors =          2
    Rotation: (unrotated)                        Number of params =         13

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      3.53309      2.56991            0.5047       0.5047
        Factor2  |      0.96318      0.14932            0.1376       0.6423
        Factor3  |      0.81386      0.30211            0.1163       0.7586
        Factor4  |      0.51175      0.05097            0.0731       0.8317
        Factor5  |      0.46078      0.04610            0.0658       0.8975
        Factor6  |      0.41468      0.11202            0.0592       0.9568
        Factor7  |      0.30266            .            0.0432       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(21) = 7911.67 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
          mein1a |   0.6624    0.4995 |      0.3117  
          mein1b |   0.7730           |      0.2986  
          mein1c |   0.8413           |      0.2517  
          mein2a |   0.4827           |      0.7006  
          mein2b |   0.7398           |      0.3101  
          mein2c |  -0.6728    0.4700 |      0.3265  
          mein2d |   0.7457           |      0.3045  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

. rotate, oblique oblimin blanks(.4) //fac1 = conspiracy, fac2 = trivialization

Factor analysis/correlation                      Number of obs    =      3,166
    Method: principal-component factors          Retained factors =          2
    Rotation: oblique oblimin (Kaiser off)       Number of params =         13

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      2.95273       0.4218
        Factor2  |      2.86191       0.4088
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(21) = 7911.67 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
          mein1a |   0.8948           |      0.3117  
          mein1b |   0.7832           |      0.2986  
          mein1c |   0.7028           |      0.2517  
          mein2a |   0.5456           |      0.7006  
          mein2b |             0.7969 |      0.3101  
          mein2c |            -0.8544 |      0.3265  
          mein2d |             0.7957 |      0.3045  
    -------------------------------------------------
    (blanks represent abs(loading)<.4)

Factor rotation matrix

    --------------------------------
                 | Factor1  Factor2 
    -------------+------------------
         Factor1 |  0.8799   0.8596 
         Factor2 |  0.4752  -0.5110 
    --------------------------------

. 
. //dropping item 2a and generate two vars for the factors
. gen mein2crec = 6-mein2c
(26 missing values generated)

. tab1 mein2c mein2crec, mis

-> tabulation of mein2c  

Aussagen zustimmen: Experten |
         glauben, dass Virus |
              gefährlich ist |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
1. Stimme überhaupt nicht zu |        163        5.06        5.06
     2. Stimme eher nicht zu |        274        8.51       13.57
            3. Unentschieden |        510       15.83       29.40
           4. Stimme eher zu |      1,123       34.86       64.27
  5. Stimme voll und ganz zu |      1,125       34.93       99.19
                           . |         26        0.81      100.00
-----------------------------+-----------------------------------
                       Total |      3,221      100.00

-> tabulation of mein2crec  

  mein2crec |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,125       34.93       34.93
          2 |      1,123       34.86       69.79
          3 |        510       15.83       85.63
          4 |        274        8.51       94.13
          5 |        163        5.06       99.19
          . |         26        0.81      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. egen conspiracy = rowmean(mein1a-mein1c)
(24 missing values generated)

. egen trivialization = rowmean(mein2b mein2crec mein2d)
(20 missing values generated)

. tab1 conspiracy trivialization, mis

-> tabulation of conspiracy  

 conspiracy |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        540       16.76       16.76
   1.333333 |        324       10.06       26.82
   1.666667 |        239        7.42       34.24
          2 |        314        9.75       43.99
   2.333333 |        277        8.60       52.59
        2.5 |          2        0.06       52.65
   2.666667 |        297        9.22       61.88
          3 |        300        9.31       71.19
   3.333333 |        233        7.23       78.42
        3.5 |          1        0.03       78.45
   3.666667 |        183        5.68       84.14
          4 |        210        6.52       90.66
   4.333333 |        112        3.48       94.13
        4.5 |          1        0.03       94.16
   4.666667 |         67        2.08       96.24
          5 |         97        3.01       99.25
          . |         24        0.75      100.00
------------+-----------------------------------
      Total |      3,221      100.00

-> tabulation of trivialization  

trivializat |
        ion |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        487       15.12       15.12
   1.333333 |        373       11.58       26.70
        1.5 |          2        0.06       26.76
   1.666667 |        310        9.62       36.39
          2 |        396       12.29       48.68
   2.333333 |        370       11.49       60.17
        2.5 |          2        0.06       60.23
   2.666667 |        338       10.49       70.72
          3 |        262        8.13       78.86
   3.333333 |        199        6.18       85.04
        3.5 |          2        0.06       85.10
   3.666667 |        166        5.15       90.25
          4 |         99        3.07       93.33
   4.333333 |         75        2.33       95.65
        4.5 |          2        0.06       95.72
   4.666667 |         69        2.14       97.86
          5 |         49        1.52       99.38
          . |         20        0.62      100.00
------------+-----------------------------------
      Total |      3,221      100.00

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Recode infection rate variables
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. d kr*

              storage   display    value
variable name   type    format     label      variable label
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
kreis           str34   %34s                  
kr_inf_md       double  %8.0g                 Confirmed Corona cases by reporting date
kr_inf_md_kum   double  %8.0g                 Cumulated confirmed Corona cases by reporting date
kr_inf_aktiv    double  %8.0g                 Active Corona cases (cumcases minus cumdead minus cumrecovered)
kr_inz          double  %8.0g                 Confirmed Corona cases in last 7 days by reporting date
kr_inz_rate     double  %8.0g                 Confirmed Corona cases by 100000 residents in last 7 days by reporting date
kr_betr_rate    double  %8.0g                 Affection rate (cumcases/residents by reporting date)
kr_neuinf_rate  double  %8.0g                 New infection rate (new infections/cumcases by reporting date)
kr_tod_md       double  %10.0g                Corona deaths by reporting date
kr_tod_md_kum   double  %10.0g                Cumulated Corona deaths by reporting date
kr_sterbe_rate  double  %10.0g                Death rate (deathcum/infectionscum)
kr_population   double  %12.0g                Number of inhabitants in Kreis

. sum kr_inz_rate kr_betr_rate kr_tod_md_kum

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 kr_inz_rate |      3,154    130.3808    61.44582         15      386.4
kr_betr_rate |      3,154    .6579146    .3085141      .1154     2.0889
kr_tod_md_~m |      3,154    81.48605     113.032          0        562

. gen double kr_inz_rate10 = kr_inz_rate/10
(67 missing values generated)

. gen double kr_tod_md_kum10 = kr_tod_md_kum/10
(67 missing values generated)

. 
. gen double kr_tod_rate = (kr_tod_md_kum*100000)/kr_population
(67 missing values generated)

. gen double kr_tod_rate10 = (kr_tod_md_kum*10000)/kr_population
(67 missing values generated)

. 
end of do-file

. 
. *Append two datasets
. keep id sample time open* zopen* znowopenindex sttwork-sttecon idtwork-idtecon ///
>    leftright* partypref trust* doubt* gentrust protransfer riskgroup* health badhealth contact ///
>    lsatisfac female age agedec old east eastchild habitation mig children* educ* ///
>    unemployed partner persweight eastaffil fullossi lagbehind incloss ///
>    kr*

. append using springdatatemp.dta , ///
>    keep(id sample time open* zopen* znowopenindex sttwork-sttecon idtwork-idtecon ///
>    leftright* partypref trust* doubt* gentrust protransfer riskgroup* health badhealth contact ///
>    lsatisfac female age agedec old east eastchild habitation mig children* educ* ///
>    unemployed partner persweight eastaffil fullossi lagbehind incloss kr*)
(note: variable id was long, now double to accommodate using data's values)
(note: variable education was byte, now float to accommodate using data's values)
(note: variable sample was byte, now float to accommodate using data's values)
(label lericat already defined)
(label _merge already defined)
(label erw1 already defined)
(label erw10 already defined)
(label erw12 already defined)
(label erw13 already defined)
(label erw16 already defined)
(label erw17 already defined)
(label erw18 already defined)
(label erw19 already defined)
(label erw2 already defined)
(label erw9 already defined)
(label erwp1 already defined)
(label erwp2 already defined)
(label ges3 already defined)
(label ges4 already defined)
(label stat1 already defined)
(label stat10 already defined)
(label stat12 already defined)
(label stat13 already defined)
(label stat14 already defined)
(label stat16 already defined)
(label stat17 already defined)
(label stat18 already defined)
(label stat2 already defined)
(label stat7 already defined)
(label stat8 already defined)
(label stat9 already defined)
(label zufr2 already defined)
(label time already defined)

. 
. *Analysis
. do descriptives.do

. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *
. * Diehl/Wolter: “ATTITUDES ABOUT CONTAINMENT MEASURES DURING
. *                THE 2020/2021 CORONAVIRUS PANDEMIC: 
. *                SELF-INTEREST, OR BROADER POLITICAL ORIENTATIONS?"
. *
. * Do-File for descriptive data analysis
. *
. * This version: 20210602
. *
. * Author: Felix Wolter, felix.wolter@uni-konstanz.de
. *
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. *Check: Fraction of respondents participating in both waves
. tab sample

                        Umfrageversion |      Freq.     Percent        Cum.
---------------------------------------+-----------------------------------
             2. Wiederholungsbefragung |      2,651       82.82       82.82
                      3. Refreshsample |        550       17.18      100.00
---------------------------------------+-----------------------------------
                                 Total |      3,201      100.00

. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Figure 2: Opposition against containment measures
. reg openkitas5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,445.9599981308)

Linear regression                               Number of obs     =      6,450
                                                F(3, 6446)        =      37.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0121
                                                Root MSE          =     1.2687

----------------------------------------------------------------------------------
                 |               Robust
      openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.0463629   .0408443    -1.14   0.256    -.1264312    .0337055
          1.east |   .4191981     .05202     8.06   0.000     .3172217    .5211745
                 |
       time#east |
november 2020#1  |   -.081466   .0730688    -1.11   0.265     -.224705     .061773
                 |
           _cons |   3.104903    .028436   109.19   0.000     3.049159    3.160647
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,450
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.104903    .028436   109.19   0.000     3.049159    3.160647
          2  |   3.524101     .04356    80.90   0.000     3.438709    3.609493
          3  |    3.05854   .0293198   104.32   0.000     3.001064    3.116017
          4  |   3.396273   .0421107    80.65   0.000     3.313721    3.478824
------------------------------------------------------------------------------

. estimates store opkitas

. 
. reg openschools5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,438.66899821162)

Linear regression                               Number of obs     =      6,441
                                                F(3, 6437)        =      51.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0203
                                                Root MSE          =     1.2687

----------------------------------------------------------------------------------
                 |               Robust
    openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.2432054   .0409308    -5.94   0.000    -.3234433   -.1629675
          1.east |   .3748539   .0516907     7.25   0.000      .273523    .4761849
                 |
       time#east |
november 2020#1  |   .0133291   .0722079     0.18   0.854    -.1282224    .1548805
                 |
           _cons |   3.287021   .0283043   116.13   0.000     3.231535    3.342506
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,441
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.287021   .0283043   116.13   0.000     3.231535    3.342506
          2  |   3.661875   .0432527    84.66   0.000     3.577085    3.746664
          3  |   3.043815   .0295668   102.95   0.000     2.985855    3.101776
          4  |   3.431998   .0408393    84.04   0.000      3.35194    3.512057
------------------------------------------------------------------------------

. estimates store opschools

. 
. reg openrestau5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,440.66399815679)

Linear regression                               Number of obs     =      6,447
                                                F(3, 6443)        =      43.38
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0170
                                                Root MSE          =     1.3693

----------------------------------------------------------------------------------
                 |               Robust
     openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.2643037   .0439319    -6.02   0.000    -.3504248   -.1781826
          1.east |   .3566713   .0528127     6.75   0.000     .2531409    .4602016
                 |
       time#east |
november 2020#1  |  -.0711095   .0787022    -0.90   0.366     -.225392     .083173
                 |
           _cons |   3.211776   .0292579   109.77   0.000     3.154421    3.269131
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,447
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.211776   .0292579   109.77   0.000     3.154421    3.269131
          2  |   3.568447   .0439676    81.16   0.000     3.482256    3.654638
          3  |   2.947472   .0327717    89.94   0.000     2.883229    3.011716
          4  |   3.233034   .0482792    66.97   0.000     3.138391    3.327677
------------------------------------------------------------------------------

. estimates store oprestau

. 
. reg opencontact5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,441.10899820924)

Linear regression                               Number of obs     =      6,446
                                                F(3, 6442)        =     123.23
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0572
                                                Root MSE          =     1.3395

----------------------------------------------------------------------------------
                 |               Robust
    opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.6076652   .0430537   -14.11   0.000    -.6920649   -.5232656
          1.east |   .3558058   .0518772     6.86   0.000     .2541091    .4575024
                 |
       time#east |
november 2020#1  |   .0539491   .0778737     0.69   0.488    -.0987092    .2066075
                 |
           _cons |   3.198347   .0287794   111.13   0.000      3.14193    3.254765
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,446
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.198347   .0287794   111.13   0.000      3.14193    3.254765
          2  |   3.554153   .0431624    82.34   0.000     3.469541    3.638766
          3  |   2.590682   .0320214    80.90   0.000      2.52791    2.653455
          4  |   3.000437   .0484531    61.92   0.000     2.905453    3.095421
------------------------------------------------------------------------------

. estimates store opcontact

. 
. reg openborders5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,438.07199810445)

Linear regression                               Number of obs     =      6,443
                                                F(3, 6439)        =      25.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0122
                                                Root MSE          =     1.4579

----------------------------------------------------------------------------------
                 |               Robust
    openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.3107624   .0470025    -6.61   0.000     -.402903   -.2186218
          1.east |   .0806306   .0568808     1.42   0.156    -.0308747    .1921358
                 |
       time#east |
november 2020#1  |  -.0696629   .0835574    -0.83   0.404    -.2334633    .0941374
                 |
           _cons |   3.047968   .0319034    95.54   0.000     2.985427     3.11051
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,443
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.047968   .0319034    95.54   0.000     2.985427     3.11051
          2  |   3.128599   .0470913    66.44   0.000     3.036284    3.220913
          3  |   2.737206   .0345168    79.30   0.000     2.669541     2.80487
          4  |   2.748173   .0505471    54.37   0.000     2.649084    2.847263
------------------------------------------------------------------------------

. estimates store opborders

. 
. reg openevents5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,451.3199981451)

Linear regression                               Number of obs     =      6,456
                                                F(3, 6452)        =      85.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0401
                                                Root MSE          =     1.2639

----------------------------------------------------------------------------------
                 |               Robust
     openevents5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |   -.468927   .0401285   -11.69   0.000    -.5475922   -.3902619
          1.east |   .2850994   .0542626     5.25   0.000     .1787268     .391472
                 |
       time#east |
november 2020#1  |   .1312399   .0761012     1.72   0.085    -.0179436    .2804234
                 |
           _cons |   2.257636   .0291902    77.34   0.000     2.200414    2.314858
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,456
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   2.257636   .0291902    77.34   0.000     2.200414    2.314858
          2  |   2.542735   .0457423    55.59   0.000     2.453065    2.632406
          3  |   1.788709   .0275359    64.96   0.000     1.734729    1.842689
          4  |   2.205048   .0457027    48.25   0.000     2.115456    2.294641
------------------------------------------------------------------------------

. estimates store opevents

. 
. coefplot (opkitas, msymb(O) mcolor(green*1.1) ciopts(lcolor(green*1.1))) ///
>    (opschools, msymb(X) mcolor(gs12) ciopts(lcolor(gs12))) ///
>    (oprestau, msymb(T) mcolor(blue) ciopts(lcolor(blue))) ///
>    (opcontact, msymb(S) mcolor(red*1.5) ciopts(lcolor(red*1.5)) ) ///
>    (opborders, msymb(D) mcolor(yellow*1.8) ciopts(lcolor(yellow*1.8)) ) ///
>    opevents, msymb(+) mcolor(black) ciopts(lcolor(black))  /// 
>    coeflabels(1._at = "May 2020, West" ///
>               2._at = "May 2020, East" ///
>                           3._at = "November 2020, West" ///
>                           4._at = "November 2020, East", labsize(3)) ///
>    graphregion(fcolor(gs15)) ///
>    xlabel(1(1)5, labsize(2.5)) ///
>    xline(1(.5)5, lcolor(gs12) lwidth(vthin)) ///
>    legend(order(2 "Daycare" 4 "Schools" 6 "Restaurants/bars" ///
>                 8 "Contacts" 10 "Borders" 12 "Events") rows(2))

. 
. 
. *Percentage values of opposition
. bysort time: sum open*dum [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
openkitas5~m |   3,256     3256.52    .3865845   .4870419          0          1
openschool~m |   3,247    3247.562    .4545767   .4980091          0          1
openrestau~m |   3,254     3253.81    .4488341   .4974516          0          1
opencontac~m |   3,255    3254.841    .4370637   .4960994          0          1
openborder~m |   3,247    3245.862    .3946757   .4888562          0          1
-------------+-----------------------------------------------------------------
openevents~m |   3,257    3256.693    .1939977   .3954878          0          1

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
openkitas5~m |   3,194     3189.44     .452317   .4977991          0          1
openschool~m |   3,194    3191.107    .4520021   .4977688          0          1
openrestau~m |   3,193    3186.854     .441425   .4966349          0          1
opencontac~m |   3,191    3186.268    .3424247   .4745952          0          1
openborder~m |   3,196     3192.21    .3657736   .4817218          0          1
-------------+-----------------------------------------------------------------
openevents~m |   3,199    3194.627    .1554961   .3624336          0          1


. 
. *Check: Correlations between subdimensions
. bysort time: pwcorr openkitas5-openevents5 [aweight=persweight], sig

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

             | openki~5 opensc~5 openre~5 openco~5 openbo~5 openev~5
-------------+------------------------------------------------------
  openkitas5 |   1.0000 
             |
             |
openschools5 |   0.8411   1.0000 
             |   0.0000
             |
 openrestau5 |   0.5599   0.5901   1.0000 
             |   0.0000   0.0000
             |
opencontact5 |   0.4980   0.5404   0.6376   1.0000 
             |   0.0000   0.0000   0.0000
             |
openborders5 |   0.3549   0.3762   0.4743   0.4951   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
 openevents5 |   0.3732   0.3652   0.4777   0.5206   0.4660   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

             | openki~5 opensc~5 openre~5 openco~5 openbo~5 openev~5
-------------+------------------------------------------------------
  openkitas5 |   1.0000 
             |
             |
openschools5 |   0.8980   1.0000 
             |   0.0000
             |
 openrestau5 |   0.3918   0.3955   1.0000 
             |   0.0000   0.0000
             |
opencontact5 |   0.3397   0.3542   0.5754   1.0000 
             |   0.0000   0.0000   0.0000
             |
openborders5 |   0.3272   0.3238   0.2888   0.3591   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
 openevents5 |   0.3023   0.3089   0.4821   0.5801   0.3537   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Figure 3: Individual threats by region and time
. reg idtecon i.time##i.east [pweight=persweight] 
(sum of wgt is 6,448.11699807644)

Linear regression                               Number of obs     =      6,451
                                                F(3, 6447)        =      13.50
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0063
                                                Root MSE          =     1.1956

----------------------------------------------------------------------------------
                 |               Robust
         idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |   .1784554   .0385516     4.63   0.000     .1028814    .2540294
          1.east |  -.0367409   .0462603    -0.79   0.427    -.1274265    .0539448
                 |
       time#east |
november 2020#1  |   .0660878    .068157     0.97   0.332    -.0675226    .1996983
                 |
           _cons |   2.357223   .0267296    88.19   0.000     2.304824    2.409622
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,451
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   2.357223   .0267296    88.19   0.000     2.304824    2.409622
          2  |   2.320482   .0377565    61.46   0.000     2.246467    2.394497
          3  |   2.535678   .0277806    91.28   0.000     2.481219    2.590138
          4  |   2.565025   .0416366    61.61   0.000     2.483404    2.646647
------------------------------------------------------------------------------

. estimates store idtecon

. 
. reg idtfamily i.time##i.east [pweight=persweight] 
(sum of wgt is 6,411.0089982748)

Linear regression                               Number of obs     =      6,416
                                                F(3, 6412)        =      28.75
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0127
                                                Root MSE          =     1.1321

----------------------------------------------------------------------------------
                 |               Robust
       idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |    .228117    .036472     6.25   0.000     .1566197    .2996144
          1.east |   .0348125   .0440144     0.79   0.429    -.0514704    .1210954
                 |
       time#east |
november 2020#1  |   .1113762   .0648473     1.72   0.086     -.015746    .2384985
                 |
           _cons |   2.053636    .024784    82.86   0.000     2.005051    2.102221
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,416
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   2.053636    .024784    82.86   0.000     2.005051    2.102221
          2  |   2.088449   .0363733    57.42   0.000     2.017145    2.159752
          3  |   2.281753   .0267575    85.28   0.000     2.229299    2.334207
          4  |   2.427942   .0393946    61.63   0.000     2.350715    2.505168
------------------------------------------------------------------------------

. estimates store idtfamily

. 
. logit childrenathome i.time##i.east [pweight=persweight] 

Iteration 0:   log pseudolikelihood = -3062.0179  
Iteration 1:   log pseudolikelihood =  -3060.745  
Iteration 2:   log pseudolikelihood =  -3060.744  
Iteration 3:   log pseudolikelihood =  -3060.744  

Logistic regression                             Number of obs     =      6,375
                                                Wald chi2(3)      =       3.36
                                                Prob > chi2       =     0.3399
Log pseudolikelihood =  -3060.744               Pseudo R2         =     0.0004

----------------------------------------------------------------------------------
                 |               Robust
  childrenathome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.0689055   .0804078    -0.86   0.391     -.226502     .088691
          1.east |  -.1117938   .0987052    -1.13   0.257    -.3052524    .0816649
                 |
       time#east |
november 2020#1  |   .0067151   .1423491     0.05   0.962    -.2722839    .2857141
                 |
           _cons |  -1.422911   .0555996   -25.59   0.000    -1.531884   -1.313937
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,375
Model VCE    : Robust

Expression   : Pr(childrenathome), predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1942057   .0087008    22.32   0.000     .1771525    .2112589
          2  |   .1773064   .0118965    14.90   0.000     .1539897    .2006231
          3  |   .1836493   .0087085    21.09   0.000     .1665809    .2007177
          4  |    .168416   .0118395    14.22   0.000      .145211    .1916211
------------------------------------------------------------------------------

. estimates store childrenathome

. 
. logit incloss i.time##i.east [pweight=persweight] 

Iteration 0:   log pseudolikelihood = -2696.1445  
Iteration 1:   log pseudolikelihood = -2692.9259  
Iteration 2:   log pseudolikelihood = -2692.9185  
Iteration 3:   log pseudolikelihood = -2692.9185  

Logistic regression                             Number of obs     =      6,447
                                                Wald chi2(3)      =       7.55
                                                Prob > chi2       =     0.0562
Log pseudolikelihood = -2692.9185               Pseudo R2         =     0.0012

----------------------------------------------------------------------------------
                 |               Robust
         incloss |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.1468603   .0893545    -1.64   0.100     -.321992    .0282713
          1.east |  -.0285723   .1061238    -0.27   0.788    -.2365711    .1794264
                 |
       time#east |
november 2020#1  |  -.1160191   .1592222    -0.73   0.466    -.4280888    .1960507
                 |
           _cons |  -1.669661   .0610808   -27.34   0.000    -1.789377   -1.549944
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,447
Model VCE    : Robust

Expression   : Pr(incloss), predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1584694   .0081455    19.45   0.000     .1425044    .1744344
          2  |   .1546962   .0113483    13.63   0.000      .132454    .1769384
          3  |   .1398518   .0078453    17.83   0.000     .1244754    .1552283
          4  |   .1233467   .0107242    11.50   0.000     .1023276    .1443658
------------------------------------------------------------------------------

. estimates store incloss

. 
. logit riskgroup01 i.time##i.east [pweight=persweight] 

Iteration 0:   log pseudolikelihood = -4133.1603  
Iteration 1:   log pseudolikelihood = -4130.5466  
Iteration 2:   log pseudolikelihood = -4130.5464  

Logistic regression                             Number of obs     =      6,209
                                                Wald chi2(3)      =       5.75
                                                Prob > chi2       =     0.1246
Log pseudolikelihood = -4130.5464               Pseudo R2         =     0.0006

----------------------------------------------------------------------------------
                 |               Robust
     riskgroup01 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |   .0861091   .0674671     1.28   0.202     -.046124    .2183422
          1.east |   .1481435   .0858698     1.73   0.084    -.0201583    .3164452
                 |
       time#east |
november 2020#1  |  -.0535458   .1211626    -0.44   0.659    -.2910202    .1839286
                 |
           _cons |  -.5070685   .0477607   -10.62   0.000    -.6006778   -.4134592
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,209
Model VCE    : Robust

Expression   : Pr(riskgroup01), predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .375881   .0112044    33.55   0.000     .3539208    .3978412
          2  |   .4112198    .017278    23.80   0.000     .3773555    .4450841
          3  |   .3962872   .0114005    34.76   0.000     .3739427    .4186317
          4  |   .4191261   .0172771    24.26   0.000     .3852636    .4529886
------------------------------------------------------------------------------

. estimates store riskgroup01

. 
. logit old i.time##i.east [pweight=persweight] 

Iteration 0:   log pseudolikelihood = -2832.8764  
Iteration 1:   log pseudolikelihood = -2830.2542  
Iteration 2:   log pseudolikelihood = -2830.2441  
Iteration 3:   log pseudolikelihood = -2830.2441  

Logistic regression                             Number of obs     =      6,459
                                                Wald chi2(3)      =       5.70
                                                Prob > chi2       =     0.1271
Log pseudolikelihood = -2830.2441               Pseudo R2         =     0.0009

----------------------------------------------------------------------------------
                 |               Robust
             old |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.0276448   .0941173    -0.29   0.769    -.2121113    .1568217
          1.east |   .1474088   .1256694     1.17   0.241    -.0988988    .3937163
                 |
       time#east |
november 2020#1  |   .1091407   .1762331     0.62   0.536    -.2362698    .4545512
                 |
           _cons |  -1.681118   .0653682   -25.72   0.000    -1.809237   -1.552999
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,459
Model VCE    : Robust

Expression   : Pr(old), predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1569475   .0086492    18.15   0.000     .1399954    .1738996
          2  |   .1774516   .0156662    11.33   0.000     .1467464    .2081568
          3  |   .1533242   .0087902    17.44   0.000     .1360957    .1705528
          4  |   .1896612   .0158831    11.94   0.000     .1585308    .2207915
------------------------------------------------------------------------------

. estimates store old

. 
. *Version 1 horizontally
. coefplot (idtecon, msymb(O) mcolor(red*1.5) ciopts(lcolor(red*1.5)))  ///
>    idtfamily, msymb(X) mcolor(blue) ciopts(lcolor(blue))  ///
>    coeflabels(1._at = "May 2020, West" ///
>               2._at = "May 2020, East" ///
>                           3._at = "November 2020, West" ///
>                           4._at = "November 2020, East", labsize(3.5)) ///
>    title("Continuous variables", size(4.5) color(black)) ///
>    graphregion(fcolor(gs15)) ///
>    xlabel(2(.5)3, labsize(2.5)) ///
>    xline(2(.2)3, lcolor(gs12) lwidth(vthin))  ///
>    legend(order(2 "Indiv. threat economic" 4 "Indiv. threat family") rows(2)) ///
>    saving(figure2a.gph, replace)
(file figure2a.gph saved)

. 
. 
. coefplot (childrenathome, msymb(T) mcolor(green*1.1) ciopts(lcolor(green*1.1))) ///
>    (incloss, msymb(S) mcolor(black) ciopts(lcolor(black))) ///
>    (riskgroup01, msymb(+) mcolor(yellow*1.8) ciopts(lcolor(yellow*1.8)))  ///
>    old, msymb(D) mcolor(gs12) ciopts(lcolor(gs12))  ///
>    coeflabels(, nolabels) ///
>    title("Binary variables", size(4.5) color(black)) ///
>    graphregion(fcolor(gs15)) ///
>    xlabel(0(.1).5, labsize(2.5)) ///
>    xline(0(.1).5, lcolor(gs12) lwidth(vthin))  ///
>    legend(order(2 "Children at home" 4 "Income loss" 6 "Covid-19 at-risk group" ///
>                 8 "Age >70") rows(2)) ///
>    saving(figure2b.gph, replace)
(file figure2b.gph saved)

.    
. graph combine "figure2a" "figure2b"

. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table 2
. 
. bysort time east: sum sttecon [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   2,258    2759.643    3.810834    .856456          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   1,000  498.332999    3.804881   .8854729          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   2,227    2704.269    3.834394    .858304          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |     973  491.992005    3.879164   .8833133          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |       0           0


. bysort time: sum sttecon [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   3,258    3257.976    3.809924    .860863          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   3,200    3196.261    3.841286   .8622531          1          5


. 
. bysort time east: sum sttfamily [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   2,240    2738.395    3.546251   .9396018          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |     991  493.157999    3.558807   .9672946          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   2,220    2697.356    3.569247   .9403674          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |     969  490.008005    3.663728   .9625344          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |       0           0


. bysort time: sum sttfamily [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   3,231    3231.553    3.548167    .943784          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   3,189    3187.364    3.583772   .9443159          1          5


. 
. bysort time east: sum doubtstate [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   2,258    2759.643    3.507613   1.318255          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   1,000  498.332999    3.725783   1.397455          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   2,226    2702.962    3.476376   1.358789          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |     974  492.879005    3.686932   1.342688          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |       0           0


. bysort time: sum doubtstate [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   3,258    3257.976    3.540983   1.332838          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   3,200    3195.841    3.508849   1.358297          1          7


. 
. bysort time east: sum protransfer [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   2,243    2739.268    4.050865   2.703425          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |     989  492.366999    4.491745   2.722839          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   2,214    2686.558    3.966043   2.737881          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |     964  487.395005    4.321915   2.768971          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |       0           0


. bysort time: sum protransfer [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   3,232    3231.635    4.118037   2.710725          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   3,178    3173.953    4.020691   2.745365          0         10


. 
. bysort time east: tab leftrightcat [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left | 494.174999       17.96       17.96
      2. mid |  1,279.148       46.49       64.45
    3. right |    325.879       11.84       76.30
4. DK and NR | 652.062999       23.70      100.00
-------------+-----------------------------------
       Total |  2,751.265      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |    103.046       20.76       20.76
      2. mid |230.7149992       46.48       67.24
    3. right |     66.998       13.50       80.74
4. DK and NR | 95.6249999       19.26      100.00
-------------+-----------------------------------
       Total | 496.383999      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |    470.022       17.41       17.41
      2. mid |   1,231.35       45.62       63.04
    3. right | 358.695999       13.29       76.33
4. DK and NR | 638.964999       23.67      100.00
-------------+-----------------------------------
       Total |  2,699.033      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |94.50400123       19.21       19.21
      2. mid |227.6960021       46.29       65.50
    3. right | 71.9330007       14.62       80.13
4. DK and NR | 97.7480008       19.87      100.00
-------------+-----------------------------------
       Total | 491.881005      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations


. bysort time: tab leftrightcat [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left | 597.220999       18.39       18.39
      2. mid |  1,509.863       46.49       64.88
    3. right |    392.877       12.10       76.98
4. DK and NR | 747.687999       23.02      100.00
-------------+-----------------------------------
       Total |  3,247.649      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left | 564.526002       17.69       17.69
      2. mid |  1,459.046       45.73       63.42
    3. right |    430.629       13.50       76.91
4. DK and NR | 736.712999       23.09      100.00
-------------+-----------------------------------
       Total |  3,190.914      100.00


. 
. bysort time: tab east [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

       east |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  2,759.643       84.70       84.70
          1 | 498.332999       15.30      100.00
------------+-----------------------------------
      Total |  3,257.976      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

       east |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  2,704.269       84.58       84.58
          1 | 492.879005       15.42      100.00
------------+-----------------------------------
      Total |  3,197.148      100.00


. 
. bysort time east: sum gentrust [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   2,248    2747.689    4.151285   2.519669          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |     997  497.169999    4.135388   2.564668          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   2,221        2696    4.233903   2.580747          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |     973  492.384005    4.337495   2.584645          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |       0           0


. bysort time: sum gentrust [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   3,245    3244.859    4.148849   2.526338          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   3,194    3188.384    4.249901   2.581329          0         10


. 
. bysort time east: tab female [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,354.578       49.09       49.09
          1 |  1,405.065       50.91      100.00
------------+-----------------------------------
      Total |  2,759.643      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 | 244.785998       49.12       49.12
          1 |    253.547       50.88      100.00
------------+-----------------------------------
      Total | 498.332999      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,324.156       48.97       48.97
          1 |  1,380.113       51.03      100.00
------------+-----------------------------------
      Total |  2,704.269      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 | 239.944003       48.68       48.68
          1 | 252.935002       51.32      100.00
------------+-----------------------------------
      Total | 492.879005      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations


. bysort time: tab female [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,599.364       49.09       49.09
          1 |  1,658.612       50.91      100.00
------------+-----------------------------------
      Total |  3,257.976      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    1,564.1       48.92       48.92
          1 |  1,633.048       51.08      100.00
------------+-----------------------------------
      Total |  3,197.148      100.00


. 
. bysort time east: sum educyr [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   2,241     2737.88    13.57277   2.813111          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |     987  491.829999    13.87939   2.366146          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   2,213    2685.175    13.63528   2.745723          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |     971  491.528005    13.95482   2.326943          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |       0           0


. bysort time: sum educyr [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   3,228     3229.71    13.61946   2.751671          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   3,184    3176.703    13.68472   2.687411          7         22


. 
. bysort time east: sum kr_inz_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   2,213    2701.831    .5479565   .5943786          0       8.03

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |     991  493.625999    .2318989   .5189215          0       6.58

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   2,192    2659.595    14.52052   5.396691       1.83      38.64

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |     962  486.069005    8.914234   6.024722        1.5      36.57

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |       0           0


. bysort time: sum kr_inz_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   3,204    3195.457    .4991328   .5943816          0       8.03

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   3,154    3145.664    13.65423   5.859408        1.5      38.64


. 
. bysort time east: sum kr_tod_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   2,213    2701.831    1.136842   1.092035          0   10.04373

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |     991  493.625999    .4823094   .6878416          0   4.825561

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   2,192    2659.595    1.670292    1.20086          0   20.26483

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |     962  486.069005    .9870068   .9932571          0   5.420912

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |       0           0


. bysort time: sum kr_tod_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   3,204    3195.457    1.035731    1.06639          0   10.04373

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   3,154    3145.664    1.564711   1.196835          0   20.26483


. 
. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table S1 (supplement): Opposition against confinement measures
. bysort time east: reg openkitas5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,758.68499928713)

Linear regression                               Number of obs     =      2,257
                                                F(0, 2256)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2543

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.104903   .0284334   109.20   0.000     3.049145    3.160662
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.8349986970425)

Linear regression                               Number of obs     =        999
                                                F(0, 998)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2907

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.524101   .0435683    80.89   0.000     3.438605    3.609597
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,697.0439953804)

Linear regression                               Number of obs     =      2,221
                                                F(0, 2220)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2855

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    3.05854   .0293173   104.33   0.000     3.001048    3.116033
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.3960047662258)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2316

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.396273   .0421193    80.63   0.000     3.313617    3.478928
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg openkitas5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,256.51999798417)

Linear regression                               Number of obs     =      3,256
                                                F(0, 3255)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2688

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.168987   .0250825   126.34   0.000     3.119808    3.218167
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,189.44000014663)

Linear regression                               Number of obs     =      3,194
                                                F(0, 3193)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      1.283

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.110681   .0257123   120.98   0.000     3.060266    3.161095
------------------------------------------------------------------------------

. 
. bysort time east: reg openschools5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,751.88199932873)

Linear regression                               Number of obs     =      2,251
                                                F(0, 2250)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2592

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.287021   .0283018   116.14   0.000      3.23152    3.342521
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 495.6799986958504)

Linear regression                               Number of obs     =        996
                                                F(0, 995)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2782

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.661875    .043261    84.65   0.000     3.576981    3.746768
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,698.69299541414)

Linear regression                               Number of obs     =      2,221
                                                F(0, 2220)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2881

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.043815   .0295643   102.96   0.000     2.985839    3.101792
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.4140047729015)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2027

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.431998   .0408477    84.02   0.000     3.351839    3.512158
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg openschools5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,247.56199802458)

Linear regression                               Number of obs     =      3,247
                                                F(0, 3246)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2691

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.344235   .0249317   134.14   0.000     3.295352    3.393119
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,191.10700018704)

Linear regression                               Number of obs     =      3,194
                                                F(0, 3193)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2828

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.103715   .0259039   119.82   0.000     3.052925    3.154505
------------------------------------------------------------------------------

. 
. bysort time east: reg openrestau5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,755.87099927664)

Linear regression                               Number of obs     =      2,255
                                                F(0, 2254)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3004

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.211776   .0292553   109.78   0.000     3.154406    3.269146
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.9389986991882)

Linear regression                               Number of obs     =        999
                                                F(0, 998)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3006

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.568447    .043976    81.15   0.000     3.482151    3.654743
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,694.45799541473)

Linear regression                               Number of obs     =      2,220
                                                F(0, 2219)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4387

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.947472   .0327689    89.95   0.000     2.883211    3.011733
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.3960047662258)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4224

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.233034    .048289    66.95   0.000     3.138271    3.327797
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg openrestau5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,253.80999797583)

Linear regression                               Number of obs     =      3,254
                                                F(0, 3253)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3066

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.266358   .0257344   126.93   0.000     3.215901    3.316816
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,186.85400018096)

Linear regression                               Number of obs     =      3,193
                                                F(0, 3192)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4397

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.991594   .0287383   104.10   0.000     2.935247    3.047941
------------------------------------------------------------------------------

. 
. bysort time east: reg opencontact5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,756.96599924564)

Linear regression                               Number of obs     =      2,256
                                                F(0, 2255)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2643

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.198347   .0287769   111.14   0.000     3.141915    3.254779
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.8749986886978)

Linear regression                               Number of obs     =        999
                                                F(0, 998)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2774

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.554153   .0431706    82.33   0.000     3.469438    3.638869
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,696.61099553108)

Linear regression                               Number of obs     =      2,221
                                                F(0, 2220)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4059

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.590682   .0320187    80.91   0.000     2.527893    2.653472
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 489.6570047438145)

Linear regression                               Number of obs     =        970
                                                F(0, 969)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4338

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.000437    .048463    61.91   0.000     2.905332    3.095542
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg opencontact5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,254.84099793434)

Linear regression                               Number of obs     =      3,255
                                                F(0, 3254)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2726

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.252773    .025313   128.50   0.000     3.203142    3.302404
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,186.2680002749)

Linear regression                               Number of obs     =      3,191
                                                F(0, 3190)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4178

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.653652   .0281794    94.17   0.000     2.598401    2.708904
------------------------------------------------------------------------------

. 
. bysort time east: reg openborders5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,747.52899926901)

Linear regression                               Number of obs     =      2,247
                                                F(0, 2246)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4045

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.047968   .0319006    95.55   0.000      2.98541    3.110526
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4005

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.128599   .0471003    66.42   0.000     3.036172    3.221026
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,699.74399535358)

Linear regression                               Number of obs     =      2,223
                                                F(0, 2222)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.5139

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.737206   .0345139    79.31   0.000     2.669523    2.804889
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.4660047888756)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      1.493

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.748173   .0505574    54.36   0.000     2.648959    2.847388
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg openborders5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,245.861997962)

Linear regression                               Number of obs     =      3,247
                                                F(0, 3246)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4041

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.060347   .0279554   109.47   0.000     3.005535    3.115159
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,192.21000014246)

Linear regression                               Number of obs     =      3,196
                                                F(0, 3195)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.5106

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.738898   .0302111    90.66   0.000     2.679663    2.798133
------------------------------------------------------------------------------

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table S2 (supplement): Descriptive analysis: self-interest indicators (figure 3 from main article)
. 
. bysort time east: reg idtecon [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,758.0779992938)

Linear regression                               Number of obs     =      2,256
                                                F(0, 2255)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1751

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.357223   .0267272    88.20   0.000     2.304811    2.409636
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 496.0119987130165)

Linear regression                               Number of obs     =        997
                                                F(0, 996)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1421

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.320482   .0377637    61.45   0.000     2.246377    2.394588
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,702.03499531746)

Linear regression                               Number of obs     =      2,225
                                                F(0, 2224)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2187

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.535678   .0277782    91.28   0.000     2.481205    2.590152
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 491.9920047521591)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2332

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.565025   .0416451    61.59   0.000     2.483301     2.64675
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg idtecon [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,254.08999800682)

Linear regression                               Number of obs     =      3,253
                                                F(0, 3252)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1701

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.351623   .0233719   100.62   0.000     2.305798    2.397448
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,194.02700006962)

Linear regression                               Number of obs     =      3,198
                                                F(0, 3197)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2208

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.540199   .0243565   104.29   0.000     2.492443    2.587955
------------------------------------------------------------------------------

. 
. bysort time east: reg incloss [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,745.04699918628)

Linear regression                               Number of obs     =      2,247
                                                F(0, 2246)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36526

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1584694   .0081467    19.45   0.000     .1424935    .1744453
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.9049986898899)

Linear regression                               Number of obs     =        999
                                                F(0, 998)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      .3618

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1546962   .0113531    13.63   0.000     .1324175    .1769748
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,704.26899540424)

Linear regression                               Number of obs     =      2,227
                                                F(0, 2226)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .34691

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1398518   .0078464    17.82   0.000     .1244648    .1552389
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.8790047764778)

Linear regression                               Number of obs     =        974
                                                F(0, 973)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =       .329

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1233467   .0107289    11.50   0.000     .1022922    .1444012
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg incloss [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,242.95199787617)

Linear regression                               Number of obs     =      3,246
                                                F(0, 3245)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36469

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1578901   .0071123    22.20   0.000      .143945    .1718351
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,197.14800018072)

Linear regression                               Number of obs     =      3,201
                                                F(0, 3200)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .34423

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1373074     .00684    20.07   0.000     .1238962    .1507186
------------------------------------------------------------------------------

. 
. bysort time east: reg idtfamily [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,738.30999937654)

Linear regression                               Number of obs     =      2,240
                                                F(0, 2239)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.0967

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.053636   .0247818    82.87   0.000     2.005038    2.102234
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 494.6379987895489)

Linear regression                               Number of obs     =        994
                                                F(0, 993)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.0789

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.088449   .0363803    57.41   0.000     2.017057     2.15984
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,688.89399543405)

Linear regression                               Number of obs     =      2,215
                                                F(0, 2214)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1719

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.281753   .0267552    85.28   0.000     2.229285    2.334221
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 489.1670046746731)

Linear regression                               Number of obs     =        967
                                                F(0, 966)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1559

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.427942   .0394027    61.62   0.000     2.350617    2.505267
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg idtfamily [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,232.94799816608)

Linear regression                               Number of obs     =      3,234
                                                F(0, 3233)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      1.094

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.058962    .021716    94.81   0.000     2.016384    2.101541
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,178.06100010872)

Linear regression                               Number of obs     =      3,182
                                                F(0, 3181)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1705

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.304254   .0234483    98.27   0.000     2.258279     2.35023
------------------------------------------------------------------------------

. 
. bysort time east: reg childrenathome [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,730.91399943829)

Linear regression                               Number of obs     =      2,236
                                                F(0, 2235)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .39568

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1942057    .008702    22.32   0.000     .1771407    .2112706
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 492.7909988164902)

Linear regression                               Number of obs     =        990
                                                F(0, 989)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .38212

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1773064   .0119016    14.90   0.000     .1539512    .2006616
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,656.88499568403)

Linear regression                               Number of obs     =      2,190
                                                F(0, 2189)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .38729

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1836493   .0087098    21.09   0.000     .1665689    .2007297
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 483.3150045573711)

Linear regression                               Number of obs     =        959
                                                F(0, 958)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .37443

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .168416   .0118448    14.22   0.000     .1451714    .1916607
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg childrenathome [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,223.70499825478)

Linear regression                               Number of obs     =      3,226
                                                F(0, 3225)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .39364

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1916224   .0075923    25.24   0.000     .1767362    .2065085
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,140.2000002414)

Linear regression                               Number of obs     =      3,149
                                                F(0, 3148)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .38533

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1813047   .0075915    23.88   0.000       .16642    .1961894
------------------------------------------------------------------------------

. 
. bysort time east: reg riskgroup01 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,637.53699925542)

Linear regression                               Number of obs     =      2,168
                                                F(0, 2167)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .48446

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .375881   .0112061    33.54   0.000     .3539052    .3978568
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 476.28299883008)

Linear regression                               Number of obs     =        956
                                                F(0, 955)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .49231

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4112198   .0172857    23.79   0.000     .3772975    .4451421
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,586.94199559093)

Linear regression                               Number of obs     =      2,140
                                                F(0, 2139)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .48924

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .3962872   .0114022    34.76   0.000     .3739266    .4186478
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 477.6510046720505)

Linear regression                               Number of obs     =        945
                                                F(0, 944)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .49368

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4191261   .0172849    24.25   0.000     .3852049    .4530473
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg riskgroup01 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,113.8199980855)

Linear regression                               Number of obs     =      3,124
                                                F(0, 3123)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .48578

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .3812863   .0098592    38.67   0.000     .3619552    .4006174
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,064.59300026298)

Linear regression                               Number of obs     =      3,085
                                                F(0, 3084)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .48995

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .3998469   .0099972    40.00   0.000      .380245    .4194488
------------------------------------------------------------------------------

. 
. bysort time east: reg old [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,759.64299929142)

Linear regression                               Number of obs     =      2,258
                                                F(0, 2257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36383

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1569475   .0086504    18.14   0.000     .1399838    .1739111
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .38224

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1774516   .0156728    11.32   0.000     .1466962    .2082071
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,704.26899540424)

Linear regression                               Number of obs     =      2,227
                                                F(0, 2226)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36038

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1533242   .0087915    17.44   0.000     .1360838    .1705647
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.8790047764778)

Linear regression                               Number of obs     =        974
                                                F(0, 973)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .39223

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1896612     .01589    11.94   0.000     .1584785    .2208438
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg old [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36674

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1600837   .0077132    20.75   0.000     .1449604    .1752071
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,197.14800018072)

Linear regression                               Number of obs     =      3,201
                                                F(0, 3200)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36566

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .158926   .0078351    20.28   0.000     .1435638    .1742883
------------------------------------------------------------------------------

. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table S3 (supplement): Descriptive analysis: Sociotropic threat, political orientations, infection rates and control variables 
. 
. bysort time east: reg sttecon [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,759.64299929142)

Linear regression                               Number of obs     =      2,258
                                                F(0, 2257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .85646

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.810834   .0196726   193.71   0.000     3.772256    3.849413
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .88547

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.804881   .0298019   127.67   0.000       3.7464    3.863363
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,704.26899540424)

Linear regression                               Number of obs     =      2,227
                                                F(0, 2226)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      .8583

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.834394   .0194661   196.98   0.000     3.796221    3.872568
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 491.9920047521591)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .88331

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.879164   .0303749   127.71   0.000     3.819556    3.938772
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg sttecon [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .86086

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.809924   .0172739   220.56   0.000     3.776055    3.843793
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,196.2610001564)

Linear regression                               Number of obs     =      3,200
                                                F(0, 3199)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .86225

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.841286   .0171199   224.38   0.000     3.807719    3.874853
------------------------------------------------------------------------------

. 
. bysort time east: reg sttfamily [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,738.39499929547)

Linear regression                               Number of obs     =      2,240
                                                F(0, 2239)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      .9396

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.546251   .0215044   164.91   0.000     3.504081    3.588422
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 493.1579987406731)

Linear regression                               Number of obs     =        991
                                                F(0, 990)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .96729

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.558807   .0327976   108.51   0.000     3.494446    3.623168
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,697.35599538684)

Linear regression                               Number of obs     =      2,220
                                                F(0, 2219)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .94037

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.569247   .0214152   166.67   0.000     3.527251    3.611243
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 490.0080046951771)

Linear regression                               Number of obs     =        969
                                                F(0, 968)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .96253

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.663728   .0331672   110.46   0.000      3.59864    3.728816
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg sttfamily [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,231.55299803615)

Linear regression                               Number of obs     =      3,231
                                                F(0, 3230)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .94378

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.548167   .0188968   187.77   0.000     3.511117    3.585218
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,187.36400008202)

Linear regression                               Number of obs     =      3,189
                                                F(0, 3188)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .94432

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.583772   .0188314   190.31   0.000     3.546849    3.620695
------------------------------------------------------------------------------

. 
. bysort time east: reg doubtstate [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,759.64299929142)

Linear regression                               Number of obs     =      2,258
                                                F(0, 2257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3183

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.507613   .0298085   117.67   0.000     3.449158    3.566067
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3975

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.725783    .047439    78.54   0.000     3.632691    3.818874
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,702.96199536324)

Linear regression                               Number of obs     =      2,226
                                                F(0, 2225)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3588

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.476376   .0311322   111.67   0.000     3.415324    3.537427
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.8790047764778)

Linear regression                               Number of obs     =        974
                                                F(0, 973)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3427

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.686932    .045711    80.66   0.000     3.597229    3.776636
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg doubtstate [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3328

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.540983   .0262919   134.68   0.000     3.489433    3.592534
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,195.84100013971)

Linear regression                               Number of obs     =      3,200
                                                F(0, 3199)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3583

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.508849   .0272795   128.63   0.000     3.455362    3.562336
------------------------------------------------------------------------------

. 
. bysort time east: reg protransfer [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,739.26799911261)

Linear regression                               Number of obs     =      2,243
                                                F(0, 2242)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7034

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.050865   .0623895    64.93   0.000     3.928518    4.173212
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 492.36699873209)

Linear regression                               Number of obs     =        989
                                                F(0, 988)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7228

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.491745   .0925495    48.53   0.000     4.310129    4.673361
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,686.55799567699)

Linear regression                               Number of obs     =      2,214
                                                F(0, 2213)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7379

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.966043   .0623678    63.59   0.000     3.843738    4.088349
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 487.3950047194958)

Linear regression                               Number of obs     =        964
                                                F(0, 963)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      2.769

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.321915   .0949606    45.51   0.000     4.135562    4.508269
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg protransfer [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,231.6349978447)

Linear regression                               Number of obs     =      3,232
                                                F(0, 3231)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7107

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.118037   .0547717    75.19   0.000     4.010646    4.225428
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,173.95300039649)

Linear regression                               Number of obs     =      3,178
                                                F(0, 3177)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7454

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.020691    .054786    73.39   0.000     3.913272    4.128111
------------------------------------------------------------------------------

. 
. mlogit leftrightcat [pweight=persweight] if time==0 & east==0 

Iteration 0:   log pseudolikelihood = -3462.0719  
Iteration 1:   log pseudolikelihood = -3462.0719  

Multinomial logistic regression                 Number of obs     =      2,253
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -3462.0719               Pseudo R2         =     0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.9510598   .0639969   -14.86   0.000    -1.076491   -.8256282
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.367423   .0738595   -18.51   0.000    -1.512185   -1.222661
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.6738083   .0568055   -11.86   0.000     -.785145   -.5624716
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =      2,253
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1796174   .0088284    20.35   0.000      .162314    .1969208
          2  |   .4649309   .0113755    40.87   0.000     .4426353    .4872264
          3  |    .118447   .0073165    16.19   0.000     .1041069     .132787
          4  |   .2370048   .0095367    24.85   0.000     .2183131    .2556964
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==0 & east==1 

Iteration 0:   log pseudolikelihood = -630.43474  
Iteration 1:   log pseudolikelihood = -630.43474  

Multinomial logistic regression                 Number of obs     =        997
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -630.43474               Pseudo R2         =    -0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.8060077   .0909909    -8.86   0.000    -.9843466   -.6276688
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |   -1.23652   .1030276   -12.00   0.000    -1.438451    -1.03459
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.8807489   .0912472    -9.65   0.000     -1.05959   -.7019076
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =        997
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .2075933   .0140231    14.80   0.000     .1801086    .2350781
          2  |   .4647914   .0168682    27.55   0.000     .4317302    .4978525
          3  |   .1349721   .0113864    11.85   0.000     .1126552    .1572891
          4  |   .1926432   .0133001    14.48   0.000     .1665754     .218711
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==1 & east==0 

Iteration 0:   log pseudolikelihood = -3432.4099  
Iteration 1:   log pseudolikelihood = -3432.4099  

Multinomial logistic regression                 Number of obs     =      2,223
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -3432.4099               Pseudo R2         =     0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.9630869   .0649705   -14.82   0.000    -1.090427   -.8357471
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.233391   .0707443   -17.43   0.000    -1.372047   -1.094735
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.6560167   .0576419   -11.38   0.000    -.7689927   -.5430407
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =      2,223
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1741446   .0087334    19.94   0.000     .1570275    .1912617
          2  |   .4562189   .0114157    39.96   0.000     .4338446    .4785933
          3  |    .132898   .0076833    17.30   0.000     .1178391    .1479569
          4  |   .2367385   .0096439    24.55   0.000     .2178369    .2556401
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==1 & east==1 

Iteration 0:   log pseudolikelihood = -627.50739  
Iteration 1:   log pseudolikelihood = -627.50739  

Multinomial logistic regression                 Number of obs     =        972
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -627.50739               Pseudo R2         =     0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.8793692   .0920067    -9.56   0.000    -1.059699   -.6990394
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.152276   .1036782   -11.11   0.000    -1.355482   -.9490707
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.8456187   .0925175    -9.14   0.000     -1.02695   -.6642876
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =        972
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1921278   .0133992    14.34   0.000     .1658659    .2183897
          2  |   .4629087   .0170069    27.22   0.000     .4295758    .4962416
          3  |   .1462407   .0122622    11.93   0.000     .1222072    .1702741
          4  |   .1987229   .0138272    14.37   0.000     .1716221    .2258237
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==0 

Iteration 0:   log pseudolikelihood = -4095.7174  
Iteration 1:   log pseudolikelihood = -4095.7174  

Multinomial logistic regression                 Number of obs     =      3,250
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -4095.7174               Pseudo R2         =     0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |   -.927487   .0554258   -16.73   0.000     -1.03612   -.8188544
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.346278    .063883   -21.07   0.000    -1.471486   -1.221069
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.7027884   .0505807   -13.89   0.000    -.8019248    -.603652
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =      3,250
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1838933   .0077809    23.63   0.000      .168643    .1991437
          2  |   .4649095   .0099749    46.61   0.000     .4453591      .48446
          3  |   .1209727   .0064379    18.79   0.000     .1083547    .1335908
          4  |   .2302244   .0083305    27.64   0.000     .2138969    .2465519
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==1 

Iteration 0:   log pseudolikelihood = -4061.9333  
Iteration 1:   log pseudolikelihood = -4061.9333  

Multinomial logistic regression                 Number of obs     =      3,195
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -4061.9333               Pseudo R2         =    -0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.9495516   .0563559   -16.85   0.000    -1.060007   -.8390961
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.220291   .0615174   -19.84   0.000    -1.340863   -1.099719
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.6833397   .0511819   -13.35   0.000    -.7836543   -.5830251
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =      3,195
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1769167   .0076699    23.07   0.000      .161884    .1919494
          2  |   .4572502   .0100043    45.71   0.000     .4376422    .4768582
          3  |   .1349547   .0067686    19.94   0.000     .1216885     .148221
          4  |   .2308784   .0084305    27.39   0.000     .2143549    .2474018
------------------------------------------------------------------------------

. 
. bysort time: reg east [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =        .36

------------------------------------------------------------------------------
             |               Robust
        east |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1529578   .0052795    28.97   0.000     .1426064    .1633093
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,197.14800018072)

Linear regression                               Number of obs     =      3,201
                                                F(0, 3200)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36116

------------------------------------------------------------------------------
             |               Robust
        east |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1541621   .0053552    28.79   0.000     .1436622     .164662
------------------------------------------------------------------------------

. 
. bysort time east: reg gentrust [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,747.68899920583)

Linear regression                               Number of obs     =      2,248
                                                F(0, 2247)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5197

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.151285   .0576845    71.97   0.000     4.038164    4.264405
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.1699986755848)

Linear regression                               Number of obs     =        997
                                                F(0, 996)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5647

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.135388   .0877746    47.11   0.000     3.963144    4.307633
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,695.99999535084)

Linear regression                               Number of obs     =      2,221
                                                F(0, 2220)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5807

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.233903   .0587405    72.08   0.000     4.118711    4.349095
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.3840047717094)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5846

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.337495    .089389    48.52   0.000     4.162077    4.512912
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg gentrust [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,244.85899788141)

Linear regression                               Number of obs     =      3,245
                                                F(0, 3244)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5263

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.148849   .0506606    81.89   0.000     4.049519    4.248179
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,188.38400012255)

Linear regression                               Number of obs     =      3,194
                                                F(0, 3193)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5813

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.249901   .0515504    82.44   0.000     4.148825    4.350976
------------------------------------------------------------------------------

. 
. bysort time east: reg female [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,759.64299929142)

Linear regression                               Number of obs     =      2,258
                                                F(0, 2257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .50003

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5091474   .0113771    44.75   0.000     .4868366    .5314581
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .50017

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5087903   .0169104    30.09   0.000     .4756063    .5419743
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,704.26899540424)

Linear regression                               Number of obs     =      2,227
                                                F(0, 2226)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .50001

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .510346   .0114347    44.63   0.000     .4879223    .5327698
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.8790047764778)

Linear regression                               Number of obs     =        974
                                                F(0, 973)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .50008

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5131787   .0170708    30.06   0.000      .479679    .5466784
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg female [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .49999

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5090928   .0099771    51.03   0.000     .4895307    .5286549
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,197.14800018072)

Linear regression                               Number of obs     =      3,201
                                                F(0, 3200)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .49996

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5107827   .0100226    50.96   0.000     .4911313    .5304342
------------------------------------------------------------------------------

. 
. bysort time east: reg educyr [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,737.87999927998)

Linear regression                               Number of obs     =      2,241
                                                F(0, 2240)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.8131

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.57277   .0639601   212.21   0.000     13.44734    13.69819
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 491.8299987018108)

Linear regression                               Number of obs     =        987
                                                F(0, 986)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.3661

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.87939   .0808316   171.71   0.000     13.72077    14.03801
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,685.17499524355)

Linear regression                               Number of obs     =      2,213
                                                F(0, 2212)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7457

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.63528   .0629437   216.63   0.000     13.51184    13.75871
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 491.5280047953129)

Linear regression                               Number of obs     =        971
                                                F(0, 970)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.3269

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.95482   .0800725   174.28   0.000     13.79769    14.11196
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg educyr [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,229.70999798179)

Linear regression                               Number of obs     =      3,228
                                                F(0, 3227)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7517

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.61946   .0556235   244.85   0.000      13.5104    13.72852
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,176.70300003886)

Linear regression                               Number of obs     =      3,184
                                                F(0, 3183)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.6874

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.68472    .054654   250.39   0.000     13.57756    13.79188
------------------------------------------------------------------------------

. 
. bysort time east: reg kr_inz_rate10 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,701.83099935949)

Linear regression                               Number of obs     =      2,213
                                                F(0, 2212)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .59438

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5479565    .013914    39.38   0.000     .5206706    .5752425
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 493.6259987652302)

Linear regression                               Number of obs     =        991
                                                F(0, 990)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .51892

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2318989   .0162351    14.28   0.000     .2000398    .2637581
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,659.59499521554)

Linear regression                               Number of obs     =      2,192
                                                F(0, 2191)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     5.3967

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   14.52052   .1248849   116.27   0.000     14.27561    14.76542
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 486.069004714489)

Linear regression                               Number of obs     =        962
                                                F(0, 961)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     6.0247

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   8.914234   .2020679    44.12   0.000     8.517689    9.310779
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg kr_inz_rate10 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,195.45699812472)

Linear regression                               Number of obs     =      3,204
                                                F(0, 3203)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .59438

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4991328   .0121498    41.08   0.000     .4753106    .5229549
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,145.66399993002)

Linear regression                               Number of obs     =      3,154
                                                F(0, 3153)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     5.8594

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.65423   .1140681   119.70   0.000     13.43058    13.87789
------------------------------------------------------------------------------

. 
. bysort time east: reg kr_tod_rate10 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,701.83099935949)

Linear regression                               Number of obs     =      2,213
                                                F(0, 2212)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      1.092

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   1.136842   .0252022    45.11   0.000     1.087419    1.186264
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 493.6259987652302)

Linear regression                               Number of obs     =        991
                                                F(0, 990)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .68784

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4823094   .0223521    21.58   0.000     .4384466    .5261723
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,659.59499521554)

Linear regression                               Number of obs     =      2,192
                                                F(0, 2191)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2009

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   1.670292     .02745    60.85   0.000     1.616462    1.724123
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 486.069004714489)

Linear regression                               Number of obs     =        962
                                                F(0, 961)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .99326

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .9870068   .0336198    29.36   0.000       .92103    1.052984
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg kr_tod_rate10 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,195.45699812472)

Linear regression                               Number of obs     =      3,204
                                                F(0, 3203)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.0664

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   1.035731   .0218883    47.32   0.000     .9928147    1.078648
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,145.66399993002)

Linear regression                               Number of obs     =      3,154
                                                F(0, 3153)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1968

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   1.564711   .0241055    64.91   0.000     1.517447    1.611975
------------------------------------------------------------------------------

. 
. 
end of do-file

. do regressions.do

. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *
. * Diehl/Wolter: “ATTITUDES ABOUT CONTAINMENT MEASURES DURING
. *                THE 2020/2021 CORONAVIRUS PANDEMIC: 
. *                SELF-INTEREST, OR BROADER POLITICAL ORIENTATIONS?"
. *
. * Do-File for regression analysis
. *
. * This version: 2021602
. *
. * Author: Felix Wolter, felix.wolter@uni-konstanz.de
. *
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. 
. *Final full models, figure 3 and table S4 (supplement)
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==0
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(19, 2915)       =      18.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1151
                                                Root MSE          =     .95313

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0387569   .0236479    -1.64   0.101    -.0851252    .0076114
       1.incloss |   .0411791   .0620902     0.66   0.507     -.080566    .1629241
       idtfamily |  -.0151395   .0237214    -0.64   0.523     -.061652     .031373
1.childrenathome |  -.0126736   .0512708    -0.25   0.805    -.1132042     .087857
   1.riskgroup01 |  -.3525135   .0431559    -8.17   0.000    -.4371326   -.2678944
           1.old |  -.0691034   .0607612    -1.14   0.256    -.1882427    .0500359
         sttecon |   .0477287   .0306149     1.56   0.119    -.0123003    .1077577
       sttfamily |   .0850086    .027782     3.06   0.002     .0305343     .139483
      doubtstate |   .1830349   .0186037     9.84   0.000     .1465572    .2195125
     protransfer |    -.01333   .0081913    -1.63   0.104    -.0293914    .0027314
                 |
    leftrightcat |
        1. left  |  -.1259192    .054463    -2.31   0.021    -.2327092   -.0191293
       3. right  |  -.0182548   .0697313    -0.26   0.794    -.1549824    .1184729
   4. DK and NR  |  -.0548026    .053331    -1.03   0.304    -.1593729    .0497677
                 |
          1.east |   .3038457   .0436968     6.95   0.000     .2181659    .3895255
        gentrust |   .0386436   .0093263     4.14   0.000     .0203568    .0569304
        1.female |  -.0797826    .040936    -1.95   0.051     -.160049    .0004838
          educyr |   .0014154   .0078617     0.18   0.857    -.0139996    .0168303
   kr_inz_rate10 |  -.0095933   .0365551    -0.26   0.793    -.0812698    .0620832
   kr_tod_rate10 |   .0125349   .0214855     0.58   0.560    -.0295934    .0546631
           _cons |  -.9662276   .1852152    -5.22   0.000    -1.329394   -.6030618
----------------------------------------------------------------------------------

. 
. estimates store May

. ereturn list

scalars:
               e(rank) =  20
               e(ll_0) =  -4193.165390729501
                 e(ll) =  -4013.651454369443
               e(r2_a) =  .109372939662936
                e(rss) =  2648.138476838018
                e(mss) =  344.5833810346576
               e(rmse) =  .9531276449999381
                 e(r2) =  .1151404632302176
                  e(F) =  18.53640081511627
               e(df_r) =  2915
               e(df_m) =  19
                  e(N) =  2935

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtstate c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educyr    c.kr_inz_rat.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "robust"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "pweight"
            e(vcetype) : "Robust"

matrices:
                  e(b) :  1 x 27
                  e(V) :  27 x 27
       e(V_modelbased) :  27 x 27

functions:
             e(sample)   

. 
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==1
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(19, 2899)       =      17.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1267
                                                Root MSE          =     .93681

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0215578   .0225017    -0.96   0.338    -.0656788    .0225632
       1.incloss |  -.0992317    .062115    -1.60   0.110    -.2210258    .0225623
       idtfamily |  -.0246457   .0224904    -1.10   0.273    -.0687444    .0194531
1.childrenathome |   .0544339   .0528406     1.03   0.303    -.0491749    .1580428
   1.riskgroup01 |  -.1933677   .0419116    -4.61   0.000    -.2755472   -.1111882
           1.old |     .01391    .057794     0.24   0.810    -.0994116    .1272315
         sttecon |    .072711   .0306416     2.37   0.018     .0126294    .1327925
       sttfamily |   .0723234   .0285233     2.54   0.011     .0163954    .1282513
      doubtstate |   .1989073   .0188034    10.58   0.000      .162038    .2357766
     protransfer |  -.0253399   .0075696    -3.35   0.001    -.0401822   -.0104976
                 |
    leftrightcat |
        1. left  |  -.0547614   .0525002    -1.04   0.297    -.1577029    .0481801
       3. right  |   .0421067   .0641434     0.66   0.512    -.0836645    .1678779
   4. DK and NR  |   -.012234   .0521515    -0.23   0.815    -.1144917    .0900237
                 |
          1.east |   .2531371   .0455845     5.55   0.000     .1637558    .3425183
        gentrust |   .0464572   .0082817     5.61   0.000     .0302186    .0626957
        1.female |   -.099572   .0406831    -2.45   0.014    -.1793428   -.0198012
          educyr |  -.0109418   .0078301    -1.40   0.162     -.026295    .0044114
   kr_inz_rate10 |  -.0037708   .0035924    -1.05   0.294    -.0108147     .003273
   kr_tod_rate10 |   .0155799   .0180671     0.86   0.389    -.0198457    .0510056
           _cons |  -.9491665   .1762996    -5.38   0.000    -1.294852   -.6034813
----------------------------------------------------------------------------------

.    
. estimates store November

. ereturn list

scalars:
               e(rank) =  20
               e(ll_0) =  -4139.027564002477
                 e(ll) =  -3941.322611588155
               e(r2_a) =  .1209628704102026
                e(rss) =  2544.221924526387
                e(mss) =  369.0756239448938
               e(rmse) =  .9368140331885634
                 e(r2) =  .1266865528852561
                  e(F) =  17.67712594538185
               e(df_r) =  2899
               e(df_m) =  19
                  e(N) =  2919

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtstate c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educyr    c.kr_inz_rat.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "robust"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "pweight"
            e(vcetype) : "Robust"

matrices:
                  e(b) :  1 x 27
                  e(V) :  27 x 27
       e(V_modelbased) :  27 x 27

functions:
             e(sample)   

. 
. 
. coefplot (May, msymb(o) mcolor(blue) ciopts(lcolor(blue)) drop(_cons) ) ///
>     (November, msymb(t) mcolor(red*1.2) ciopts(lcolor(red*1.2)) drop(_cons) ) , ///
>         xline(0, lwidth(medthin) lcolor(black)) ///
>     headings(idtecon = "{bf:Individual threat:}" ///
>                  sttecon = "{bf:Sociotropic threat:}" ///
>                          doubtstate = "{bf:Political orientations:}" ///
>                          1.east = "{bf:Control variables:}" ///
>                          , labsize(2.5)) ///
>         coeflabels(idtfamily = "Family situation" ///
>                    1.childrenathome = "Children in household" ///
>                            idtecon = "Economic situation" ///
>                            1.incloss = "Income loss in pandemic" ///
>                            1.riskgroup01 = "Covid-19 at-risk group" ///
>                            badhealth = "Bad health" ///
>                            1.old = "Age >70" ///
>                            sttfamily = "Family situation" ///
>                            sttecon = "Economic situation" ///
>                            doubtstate = "Low trust in institutions" ///
>                            protransfer = "Pro redistribution" ///
>                            1.leftrightcat = "Pol. ideol.: left-wing" ///
>                            3.leftrightcat = "Pol. ideol.: right-wing" ///
>                            4.leftrightcat = "Pol. ideol.: missing" ///
>                            gentrust = "General trust" ///
>                            1.east = "East Germany" ///
>                            1.female = "Gender female" ///
>                            educyr = "Education (years)" ///
>                            kr_inz_rate10 = "Infections in last 7 days/10K pop." ///
>                            kr_tod_rate10 = "Cumulated deaths/10K pop." ///
>                            , labsize(2.5)) ///
>         xlabel(-.4(.1).4, labsize(2.2)) ylabel(, labsize(2.5)) ///
>         graphregion(fcolor(gs15)) ///
>     plotlabels("May survey" "November survey") ///
>     xtitle("Effect on opposition to containments", size(2.5)) legend( size(vsmall)) ///
>         ysize(15) xsize(10)

. 
. 
end of do-file

. do "robustness analysis descriptives.do"

. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *
. * Diehl/Wolter: “ATTITUDES ABOUT CONTAINMENT MEASURES DURING
. *                THE 2020/2021 CORONAVIRUS PANDEMIC: 
. *                SELF-INTEREST, OR BROADER POLITICAL ORIENTATIONS?"
. *
. * Do-File for descriptive data analysis
. *
. * This version: 20210602
. *
. * Author: Felix Wolter, felix.wolter@uni-konstanz.de
. *
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table S0: Unweighted and weighted sample composition
. 
. tab female

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,153       48.82       48.82
          1 |      3,306       51.18      100.00
------------+-----------------------------------
      Total |      6,459      100.00

. tab female [iweight=persweight]

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  3,163.464       49.01       49.01
          1 |   3,291.66       50.99      100.00
------------+-----------------------------------
      Total |  6,455.124      100.00

. 
. 
. tab age, mis

        age |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |          8        0.12        0.12
         19 |          6        0.09        0.22
         20 |         11        0.17        0.38
         21 |         26        0.40        0.78
         22 |         32        0.49        1.28
         23 |         37        0.57        1.85
         24 |         41        0.63        2.48
         25 |         95        1.46        3.94
         26 |        116        1.78        5.72
         27 |        129        1.98        7.71
         28 |        133        2.05        9.76
         29 |        129        1.98       11.74
         30 |         87        1.34       13.08
         31 |        106        1.63       14.71
         32 |        138        2.12       16.83
         33 |         99        1.52       18.36
         34 |        125        1.92       20.28
         35 |        153        2.35       22.63
         36 |        123        1.89       24.53
         37 |        134        2.06       26.59
         38 |        128        1.97       28.56
         39 |        128        1.97       30.53
         40 |        122        1.88       32.40
         41 |        110        1.69       34.10
         42 |        122        1.88       35.97
         43 |        136        2.09       38.07
         44 |         89        1.37       39.44
         45 |        106        1.63       41.07
         46 |         92        1.42       42.48
         47 |         73        1.12       43.61
         48 |         94        1.45       45.05
         49 |        111        1.71       46.76
         50 |        128        1.97       48.73
         51 |        110        1.69       50.42
         52 |        109        1.68       52.10
         53 |        151        2.32       54.42
         54 |        120        1.85       56.27
         55 |        157        2.42       58.69
         56 |        107        1.65       60.33
         57 |        119        1.83       62.16
         58 |         93        1.43       63.59
         59 |        111        1.71       65.30
         60 |        121        1.86       67.16
         61 |        122        1.88       69.04
         62 |         87        1.34       70.38
         63 |        126        1.94       72.32
         64 |        103        1.58       73.90
         65 |        140        2.15       76.06
         66 |        174        2.68       78.74
         67 |        123        1.89       80.63
         68 |        136        2.09       82.72
         69 |        128        1.97       84.69
         70 |        124        1.91       86.60
         71 |        161        2.48       89.08
         72 |        108        1.66       90.74
         73 |        106        1.63       92.37
         74 |         58        0.89       93.26
         75 |         37        0.57       93.83
         76 |         50        0.77       94.60
         77 |         57        0.88       95.48
         78 |         45        0.69       96.17
         79 |         56        0.86       97.03
         80 |         51        0.78       97.82
         81 |         26        0.40       98.22
         82 |         28        0.43       98.65
         83 |         21        0.32       98.97
         84 |          4        0.06       99.03
         85 |         10        0.15       99.18
         86 |          1        0.02       99.20
         87 |          6        0.09       99.29
         89 |          2        0.03       99.32
         90 |          2        0.03       99.35
         91 |          1        0.02       99.37
        100 |          1        0.02       99.38
          . |         40        0.62      100.00
------------+-----------------------------------
      Total |      6,499      100.00

. recode age (1/39.5=1) (40/59.5=2) (60/1000=3), gen(agecat)
(6459 differences between age and agecat)

. tab age agecat

           |          RECODE of age
       age |         1          2          3 |     Total
-----------+---------------------------------+----------
        18 |         8          0          0 |         8 
        19 |         6          0          0 |         6 
        20 |        11          0          0 |        11 
        21 |        26          0          0 |        26 
        22 |        32          0          0 |        32 
        23 |        37          0          0 |        37 
        24 |        41          0          0 |        41 
        25 |        95          0          0 |        95 
        26 |       116          0          0 |       116 
        27 |       129          0          0 |       129 
        28 |       133          0          0 |       133 
        29 |       129          0          0 |       129 
        30 |        87          0          0 |        87 
        31 |       106          0          0 |       106 
        32 |       138          0          0 |       138 
        33 |        99          0          0 |        99 
        34 |       125          0          0 |       125 
        35 |       153          0          0 |       153 
        36 |       123          0          0 |       123 
        37 |       134          0          0 |       134 
        38 |       128          0          0 |       128 
        39 |       128          0          0 |       128 
        40 |         0        122          0 |       122 
        41 |         0        110          0 |       110 
        42 |         0        122          0 |       122 
        43 |         0        136          0 |       136 
        44 |         0         89          0 |        89 
        45 |         0        106          0 |       106 
        46 |         0         92          0 |        92 
        47 |         0         73          0 |        73 
        48 |         0         94          0 |        94 
        49 |         0        111          0 |       111 
        50 |         0        128          0 |       128 
        51 |         0        110          0 |       110 
        52 |         0        109          0 |       109 
        53 |         0        151          0 |       151 
        54 |         0        120          0 |       120 
        55 |         0        157          0 |       157 
        56 |         0        107          0 |       107 
        57 |         0        119          0 |       119 
        58 |         0         93          0 |        93 
        59 |         0        111          0 |       111 
        60 |         0          0        121 |       121 
        61 |         0          0        122 |       122 
        62 |         0          0         87 |        87 
        63 |         0          0        126 |       126 
        64 |         0          0        103 |       103 
        65 |         0          0        140 |       140 
        66 |         0          0        174 |       174 
        67 |         0          0        123 |       123 
        68 |         0          0        136 |       136 
        69 |         0          0        128 |       128 
        70 |         0          0        124 |       124 
        71 |         0          0        161 |       161 
        72 |         0          0        108 |       108 
        73 |         0          0        106 |       106 
        74 |         0          0         58 |        58 
        75 |         0          0         37 |        37 
        76 |         0          0         50 |        50 
        77 |         0          0         57 |        57 
        78 |         0          0         45 |        45 
        79 |         0          0         56 |        56 
        80 |         0          0         51 |        51 
        81 |         0          0         26 |        26 
        82 |         0          0         28 |        28 
        83 |         0          0         21 |        21 
        84 |         0          0          4 |         4 
        85 |         0          0         10 |        10 
        86 |         0          0          1 |         1 
        87 |         0          0          6 |         6 
        89 |         0          0          2 |         2 
        90 |         0          0          2 |         2 
        91 |         0          0          1 |         1 
       100 |         0          0          1 |         1 
-----------+---------------------------------+----------
     Total |     1,984      2,260      2,215 |     6,459 

. tab agecat

  RECODE of |
        age |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,984       30.72       30.72
          2 |      2,260       34.99       65.71
          3 |      2,215       34.29      100.00
------------+-----------------------------------
      Total |      6,459      100.00

. tab agecat [iweight=persweight]

  RECODE of |
        age |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |  2,049.122       31.74       31.74
          2 |  2,268.685       35.15       66.89
          3 |  2,137.317       33.11      100.00
------------+-----------------------------------
      Total |  6,455.124      100.00

. drop agecat

. 
. tab educgencat

 educgencat |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      2,035       31.51       31.51
          2 |      2,273       35.19       66.70
          3 |      2,151       33.30      100.00
------------+-----------------------------------
      Total |      6,459      100.00

. tab educgencat [iweight=persweight]

 educgencat |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |  2,304.062       35.69       35.69
          2 |  1,976.673       30.62       66.32
          3 |  2,174.389       33.68      100.00
------------+-----------------------------------
      Total |  6,455.124      100.00

. 
. 
. *Additional info
. tab old time [iweight=persweight], col 

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

           |       Time/wave
       old | spring 20  november  |     Total
-----------+----------------------+----------
         0 | 2,736.427  2,689.038 | 5,425.465 
           |     83.99      84.11 |     84.05 
-----------+----------------------+----------
         1 |   521.549     508.11 | 1,029.659 
           |     16.01      15.89 |     15.95 
-----------+----------------------+----------
     Total | 3,257.976  3,197.148 | 6,455.124 
           |    100.00     100.00 |    100.00 

. tab old time , col 

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

           |       Time/wave
       old | spring 20  november  |     Total
-----------+----------------------+----------
         0 |     2,839      2,789 |     5,628 
           |     86.61      86.59 |     86.60 
-----------+----------------------+----------
         1 |       439        432 |       871 
           |     13.39      13.41 |     13.40 
-----------+----------------------+----------
     Total |     3,278      3,221 |     6,499 
           |    100.00     100.00 |    100.00 

. 
. gen over59 = age>59 if age~=.
(40 missing values generated)

. tab over59 time [iweight=persweight], col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

           |       Time/wave
    over59 | spring 20  november  |     Total
-----------+----------------------+----------
         0 | 2,177.862  2,139.945 | 4,317.807 
           |     66.85      66.93 |     66.89 
-----------+----------------------+----------
         1 | 1,080.114  1,057.203 | 2,137.317 
           |     33.15      33.07 |     33.11 
-----------+----------------------+----------
     Total | 3,257.976  3,197.148 | 6,455.124 
           |    100.00     100.00 |    100.00 

. tab over59 time , col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

           |       Time/wave
    over59 | spring 20  november  |     Total
-----------+----------------------+----------
         0 |     2,146      2,098 |     4,244 
           |     65.87      65.54 |     65.71 
-----------+----------------------+----------
         1 |     1,112      1,103 |     2,215 
           |     34.13      34.46 |     34.29 
-----------+----------------------+----------
     Total |     3,258      3,201 |     6,459 
           |    100.00     100.00 |    100.00 

. 
. bysort time: tab riskgroup01 old, col

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

riskgroup0 |          old
         1 |         0          1 |     Total
-----------+----------------------+----------
         0 |     1,793        139 |     1,932 
           |     65.94      34.32 |     61.84 
-----------+----------------------+----------
         1 |       926        266 |     1,192 
           |     34.06      65.68 |     38.16 
-----------+----------------------+----------
     Total |     2,719        405 |     3,124 
           |    100.00     100.00 |    100.00 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

riskgroup0 |          old
         1 |         0          1 |     Total
-----------+----------------------+----------
         0 |     1,727        123 |     1,850 
           |     64.22      31.06 |     59.97 
-----------+----------------------+----------
         1 |       962        273 |     1,235 
           |     35.78      68.94 |     40.03 
-----------+----------------------+----------
     Total |     2,689        396 |     3,085 
           |    100.00     100.00 |    100.00 


. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Note to Table S2: Composition of the over-70 years old age group
. tab age [iweight=persweight] if age>70

        age |      Freq.     Percent        Cum.
------------+-----------------------------------
         71 |    210.242       20.42       20.42
         72 | 144.966999       14.08       34.50
         73 |    124.294       12.07       46.57
         74 | 77.2120003        7.50       54.07
         75 |49.03000015        4.76       58.83
         76 | 56.6239998        5.50       64.33
         77 | 61.3999997        5.96       70.29
         78 | 55.0569999        5.35       75.64
         79 | 60.0450004        5.83       81.47
         80 | 61.2099997        5.94       87.42
         81 | 35.9300001        3.49       90.90
         82 | 33.9360002        3.30       94.20
         83 | 24.0690002        2.34       96.54
         84 | 5.41199988        0.53       97.06
         85 | 12.4770001        1.21       98.28
         86 | 1.00100005        0.10       98.37
         87 | 8.20600015        0.80       99.17
         89 | 2.65100002        0.26       99.43
         90 | 2.84499991        0.28       99.70
         91 | 1.17499995        0.11       99.82
        100 | 1.87600005        0.18      100.00
------------+-----------------------------------
      Total |  1,029.659      100.00

. 
. 
. 
. *Check: Fraction of respondents participating in both waves
. tab sample

                        Umfrageversion |      Freq.     Percent        Cum.
---------------------------------------+-----------------------------------
             2. Wiederholungsbefragung |      2,651       82.82       82.82
                      3. Refreshsample |        550       17.18      100.00
---------------------------------------+-----------------------------------
                                 Total |      3,201      100.00

. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Figure 2: Opposition against containment measures
. reg openkitas5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,445.9599981308)

Linear regression                               Number of obs     =      6,450
                                                F(3, 6446)        =      37.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0121
                                                Root MSE          =     1.2687

----------------------------------------------------------------------------------
                 |               Robust
      openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.0463629   .0408443    -1.14   0.256    -.1264312    .0337055
          1.east |   .4191981     .05202     8.06   0.000     .3172217    .5211745
                 |
       time#east |
november 2020#1  |   -.081466   .0730688    -1.11   0.265     -.224705     .061773
                 |
           _cons |   3.104903    .028436   109.19   0.000     3.049159    3.160647
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,450
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.104903    .028436   109.19   0.000     3.049159    3.160647
          2  |   3.524101     .04356    80.90   0.000     3.438709    3.609493
          3  |    3.05854   .0293198   104.32   0.000     3.001064    3.116017
          4  |   3.396273   .0421107    80.65   0.000     3.313721    3.478824
------------------------------------------------------------------------------

. estimates store opkitas

. 
. reg openschools5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,438.66899821162)

Linear regression                               Number of obs     =      6,441
                                                F(3, 6437)        =      51.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0203
                                                Root MSE          =     1.2687

----------------------------------------------------------------------------------
                 |               Robust
    openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.2432054   .0409308    -5.94   0.000    -.3234433   -.1629675
          1.east |   .3748539   .0516907     7.25   0.000      .273523    .4761849
                 |
       time#east |
november 2020#1  |   .0133291   .0722079     0.18   0.854    -.1282224    .1548805
                 |
           _cons |   3.287021   .0283043   116.13   0.000     3.231535    3.342506
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,441
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.287021   .0283043   116.13   0.000     3.231535    3.342506
          2  |   3.661875   .0432527    84.66   0.000     3.577085    3.746664
          3  |   3.043815   .0295668   102.95   0.000     2.985855    3.101776
          4  |   3.431998   .0408393    84.04   0.000      3.35194    3.512057
------------------------------------------------------------------------------

. estimates store opschools

. 
. reg openrestau5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,440.66399815679)

Linear regression                               Number of obs     =      6,447
                                                F(3, 6443)        =      43.38
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0170
                                                Root MSE          =     1.3693

----------------------------------------------------------------------------------
                 |               Robust
     openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.2643037   .0439319    -6.02   0.000    -.3504248   -.1781826
          1.east |   .3566713   .0528127     6.75   0.000     .2531409    .4602016
                 |
       time#east |
november 2020#1  |  -.0711095   .0787022    -0.90   0.366     -.225392     .083173
                 |
           _cons |   3.211776   .0292579   109.77   0.000     3.154421    3.269131
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,447
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.211776   .0292579   109.77   0.000     3.154421    3.269131
          2  |   3.568447   .0439676    81.16   0.000     3.482256    3.654638
          3  |   2.947472   .0327717    89.94   0.000     2.883229    3.011716
          4  |   3.233034   .0482792    66.97   0.000     3.138391    3.327677
------------------------------------------------------------------------------

. estimates store oprestau

. 
. reg opencontact5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,441.10899820924)

Linear regression                               Number of obs     =      6,446
                                                F(3, 6442)        =     123.23
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0572
                                                Root MSE          =     1.3395

----------------------------------------------------------------------------------
                 |               Robust
    opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.6076652   .0430537   -14.11   0.000    -.6920649   -.5232656
          1.east |   .3558058   .0518772     6.86   0.000     .2541091    .4575024
                 |
       time#east |
november 2020#1  |   .0539491   .0778737     0.69   0.488    -.0987092    .2066075
                 |
           _cons |   3.198347   .0287794   111.13   0.000      3.14193    3.254765
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,446
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.198347   .0287794   111.13   0.000      3.14193    3.254765
          2  |   3.554153   .0431624    82.34   0.000     3.469541    3.638766
          3  |   2.590682   .0320214    80.90   0.000      2.52791    2.653455
          4  |   3.000437   .0484531    61.92   0.000     2.905453    3.095421
------------------------------------------------------------------------------

. estimates store opcontact

. 
. reg openborders5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,438.07199810445)

Linear regression                               Number of obs     =      6,443
                                                F(3, 6439)        =      25.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0122
                                                Root MSE          =     1.4579

----------------------------------------------------------------------------------
                 |               Robust
    openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.3107624   .0470025    -6.61   0.000     -.402903   -.2186218
          1.east |   .0806306   .0568808     1.42   0.156    -.0308747    .1921358
                 |
       time#east |
november 2020#1  |  -.0696629   .0835574    -0.83   0.404    -.2334633    .0941374
                 |
           _cons |   3.047968   .0319034    95.54   0.000     2.985427     3.11051
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,443
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.047968   .0319034    95.54   0.000     2.985427     3.11051
          2  |   3.128599   .0470913    66.44   0.000     3.036284    3.220913
          3  |   2.737206   .0345168    79.30   0.000     2.669541     2.80487
          4  |   2.748173   .0505471    54.37   0.000     2.649084    2.847263
------------------------------------------------------------------------------

. estimates store opborders

. 
. reg openevents5 i.time##i.east [pweight=persweight] 
(sum of wgt is 6,451.3199981451)

Linear regression                               Number of obs     =      6,456
                                                F(3, 6452)        =      85.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0401
                                                Root MSE          =     1.2639

----------------------------------------------------------------------------------
                 |               Robust
     openevents5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |   -.468927   .0401285   -11.69   0.000    -.5475922   -.3902619
          1.east |   .2850994   .0542626     5.25   0.000     .1787268     .391472
                 |
       time#east |
november 2020#1  |   .1312399   .0761012     1.72   0.085    -.0179436    .2804234
                 |
           _cons |   2.257636   .0291902    77.34   0.000     2.200414    2.314858
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,456
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   2.257636   .0291902    77.34   0.000     2.200414    2.314858
          2  |   2.542735   .0457423    55.59   0.000     2.453065    2.632406
          3  |   1.788709   .0275359    64.96   0.000     1.734729    1.842689
          4  |   2.205048   .0457027    48.25   0.000     2.115456    2.294641
------------------------------------------------------------------------------

. estimates store opevents

. 
. coefplot (opkitas, msymb(O) mcolor(green*1.1) ciopts(lcolor(green*1.1))) ///
>    (opschools, msymb(X) mcolor(gs12) ciopts(lcolor(gs12))) ///
>    (oprestau, msymb(T) mcolor(blue) ciopts(lcolor(blue))) ///
>    (opcontact, msymb(S) mcolor(red*1.5) ciopts(lcolor(red*1.5)) ) ///
>    (opborders, msymb(D) mcolor(yellow*1.8) ciopts(lcolor(yellow*1.8)) ) ///
>    opevents, msymb(+) mcolor(black) ciopts(lcolor(black))  /// 
>    coeflabels(1._at = "May 2020, West" ///
>               2._at = "May 2020, East" ///
>                           3._at = "November 2020, West" ///
>                           4._at = "November 2020, East", labsize(3)) ///
>    graphregion(fcolor(gs15)) ///
>    xlabel(1(1)5, labsize(2.5)) ///
>    xline(1(.5)5, lcolor(gs12) lwidth(vthin)) ///
>    legend(order(2 "Daycare" 4 "Schools" 6 "Restaurants/bars" ///
>                 8 "Contacts" 10 "Borders" 12 "Events") rows(2))

. 
. 
. *Percentage values of opposition
. bysort time: sum open*dum [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
openkitas5~m |   3,256     3256.52    .3865845   .4870419          0          1
openschool~m |   3,247    3247.562    .4545767   .4980091          0          1
openrestau~m |   3,254     3253.81    .4488341   .4974516          0          1
opencontac~m |   3,255    3254.841    .4370637   .4960994          0          1
openborder~m |   3,247    3245.862    .3946757   .4888562          0          1
-------------+-----------------------------------------------------------------
openevents~m |   3,257    3256.693    .1939977   .3954878          0          1

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
openkitas5~m |   3,194     3189.44     .452317   .4977991          0          1
openschool~m |   3,194    3191.107    .4520021   .4977688          0          1
openrestau~m |   3,193    3186.854     .441425   .4966349          0          1
opencontac~m |   3,191    3186.268    .3424247   .4745952          0          1
openborder~m |   3,196     3192.21    .3657736   .4817218          0          1
-------------+-----------------------------------------------------------------
openevents~m |   3,199    3194.627    .1554961   .3624336          0          1


. 
. *Check: Correlations between subdimensions
. bysort time: pwcorr openkitas5-openevents5 [aweight=persweight], sig

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

             | openki~5 opensc~5 openre~5 openco~5 openbo~5 openev~5
-------------+------------------------------------------------------
  openkitas5 |   1.0000 
             |
             |
openschools5 |   0.8411   1.0000 
             |   0.0000
             |
 openrestau5 |   0.5599   0.5901   1.0000 
             |   0.0000   0.0000
             |
opencontact5 |   0.4980   0.5404   0.6376   1.0000 
             |   0.0000   0.0000   0.0000
             |
openborders5 |   0.3549   0.3762   0.4743   0.4951   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
 openevents5 |   0.3732   0.3652   0.4777   0.5206   0.4660   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

             | openki~5 opensc~5 openre~5 openco~5 openbo~5 openev~5
-------------+------------------------------------------------------
  openkitas5 |   1.0000 
             |
             |
openschools5 |   0.8980   1.0000 
             |   0.0000
             |
 openrestau5 |   0.3918   0.3955   1.0000 
             |   0.0000   0.0000
             |
opencontact5 |   0.3397   0.3542   0.5754   1.0000 
             |   0.0000   0.0000   0.0000
             |
openborders5 |   0.3272   0.3238   0.2888   0.3591   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
 openevents5 |   0.3023   0.3089   0.4821   0.5801   0.3537   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Figure 3: Individual threats by region and time
. reg idtecon i.time##i.east [pweight=persweight] 
(sum of wgt is 6,448.11699807644)

Linear regression                               Number of obs     =      6,451
                                                F(3, 6447)        =      13.50
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0063
                                                Root MSE          =     1.1956

----------------------------------------------------------------------------------
                 |               Robust
         idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |   .1784554   .0385516     4.63   0.000     .1028814    .2540294
          1.east |  -.0367409   .0462603    -0.79   0.427    -.1274265    .0539448
                 |
       time#east |
november 2020#1  |   .0660878    .068157     0.97   0.332    -.0675226    .1996983
                 |
           _cons |   2.357223   .0267296    88.19   0.000     2.304824    2.409622
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,451
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   2.357223   .0267296    88.19   0.000     2.304824    2.409622
          2  |   2.320482   .0377565    61.46   0.000     2.246467    2.394497
          3  |   2.535678   .0277806    91.28   0.000     2.481219    2.590138
          4  |   2.565025   .0416366    61.61   0.000     2.483404    2.646647
------------------------------------------------------------------------------

. estimates store idtecon

. 
. reg idtfamily i.time##i.east [pweight=persweight] 
(sum of wgt is 6,411.0089982748)

Linear regression                               Number of obs     =      6,416
                                                F(3, 6412)        =      28.75
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0127
                                                Root MSE          =     1.1321

----------------------------------------------------------------------------------
                 |               Robust
       idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |    .228117    .036472     6.25   0.000     .1566197    .2996144
          1.east |   .0348125   .0440144     0.79   0.429    -.0514704    .1210954
                 |
       time#east |
november 2020#1  |   .1113762   .0648473     1.72   0.086     -.015746    .2384985
                 |
           _cons |   2.053636    .024784    82.86   0.000     2.005051    2.102221
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,416
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   2.053636    .024784    82.86   0.000     2.005051    2.102221
          2  |   2.088449   .0363733    57.42   0.000     2.017145    2.159752
          3  |   2.281753   .0267575    85.28   0.000     2.229299    2.334207
          4  |   2.427942   .0393946    61.63   0.000     2.350715    2.505168
------------------------------------------------------------------------------

. estimates store idtfamily

. 
. logit childrenathome i.time##i.east [pweight=persweight] 

Iteration 0:   log pseudolikelihood = -3062.0179  
Iteration 1:   log pseudolikelihood =  -3060.745  
Iteration 2:   log pseudolikelihood =  -3060.744  
Iteration 3:   log pseudolikelihood =  -3060.744  

Logistic regression                             Number of obs     =      6,375
                                                Wald chi2(3)      =       3.36
                                                Prob > chi2       =     0.3399
Log pseudolikelihood =  -3060.744               Pseudo R2         =     0.0004

----------------------------------------------------------------------------------
                 |               Robust
  childrenathome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.0689055   .0804078    -0.86   0.391     -.226502     .088691
          1.east |  -.1117938   .0987052    -1.13   0.257    -.3052524    .0816649
                 |
       time#east |
november 2020#1  |   .0067151   .1423491     0.05   0.962    -.2722839    .2857141
                 |
           _cons |  -1.422911   .0555996   -25.59   0.000    -1.531884   -1.313937
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,375
Model VCE    : Robust

Expression   : Pr(childrenathome), predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1942057   .0087008    22.32   0.000     .1771525    .2112589
          2  |   .1773064   .0118965    14.90   0.000     .1539897    .2006231
          3  |   .1836493   .0087085    21.09   0.000     .1665809    .2007177
          4  |    .168416   .0118395    14.22   0.000      .145211    .1916211
------------------------------------------------------------------------------

. estimates store childrenathome

. 
. logit incloss i.time##i.east [pweight=persweight] 

Iteration 0:   log pseudolikelihood = -2696.1445  
Iteration 1:   log pseudolikelihood = -2692.9259  
Iteration 2:   log pseudolikelihood = -2692.9185  
Iteration 3:   log pseudolikelihood = -2692.9185  

Logistic regression                             Number of obs     =      6,447
                                                Wald chi2(3)      =       7.55
                                                Prob > chi2       =     0.0562
Log pseudolikelihood = -2692.9185               Pseudo R2         =     0.0012

----------------------------------------------------------------------------------
                 |               Robust
         incloss |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.1468603   .0893545    -1.64   0.100     -.321992    .0282713
          1.east |  -.0285723   .1061238    -0.27   0.788    -.2365711    .1794264
                 |
       time#east |
november 2020#1  |  -.1160191   .1592222    -0.73   0.466    -.4280888    .1960507
                 |
           _cons |  -1.669661   .0610808   -27.34   0.000    -1.789377   -1.549944
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,447
Model VCE    : Robust

Expression   : Pr(incloss), predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1584694   .0081455    19.45   0.000     .1425044    .1744344
          2  |   .1546962   .0113483    13.63   0.000      .132454    .1769384
          3  |   .1398518   .0078453    17.83   0.000     .1244754    .1552283
          4  |   .1233467   .0107242    11.50   0.000     .1023276    .1443658
------------------------------------------------------------------------------

. estimates store incloss

. 
. logit riskgroup01 i.time##i.east [pweight=persweight] 

Iteration 0:   log pseudolikelihood = -4133.1603  
Iteration 1:   log pseudolikelihood = -4130.5466  
Iteration 2:   log pseudolikelihood = -4130.5464  

Logistic regression                             Number of obs     =      6,209
                                                Wald chi2(3)      =       5.75
                                                Prob > chi2       =     0.1246
Log pseudolikelihood = -4130.5464               Pseudo R2         =     0.0006

----------------------------------------------------------------------------------
                 |               Robust
     riskgroup01 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |   .0861091   .0674671     1.28   0.202     -.046124    .2183422
          1.east |   .1481435   .0858698     1.73   0.084    -.0201583    .3164452
                 |
       time#east |
november 2020#1  |  -.0535458   .1211626    -0.44   0.659    -.2910202    .1839286
                 |
           _cons |  -.5070685   .0477607   -10.62   0.000    -.6006778   -.4134592
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,209
Model VCE    : Robust

Expression   : Pr(riskgroup01), predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .375881   .0112044    33.55   0.000     .3539208    .3978412
          2  |   .4112198    .017278    23.80   0.000     .3773555    .4450841
          3  |   .3962872   .0114005    34.76   0.000     .3739427    .4186317
          4  |   .4191261   .0172771    24.26   0.000     .3852636    .4529886
------------------------------------------------------------------------------

. estimates store riskgroup01

. 
. logit old i.time##i.east [pweight=persweight] 

Iteration 0:   log pseudolikelihood = -2832.8764  
Iteration 1:   log pseudolikelihood = -2830.2542  
Iteration 2:   log pseudolikelihood = -2830.2441  
Iteration 3:   log pseudolikelihood = -2830.2441  

Logistic regression                             Number of obs     =      6,459
                                                Wald chi2(3)      =       5.70
                                                Prob > chi2       =     0.1271
Log pseudolikelihood = -2830.2441               Pseudo R2         =     0.0009

----------------------------------------------------------------------------------
                 |               Robust
             old |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.0276448   .0941173    -0.29   0.769    -.2121113    .1568217
          1.east |   .1474088   .1256694     1.17   0.241    -.0988988    .3937163
                 |
       time#east |
november 2020#1  |   .1091407   .1762331     0.62   0.536    -.2362698    .4545512
                 |
           _cons |  -1.681118   .0653682   -25.72   0.000    -1.809237   -1.552999
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post

Adjusted predictions                            Number of obs     =      6,459
Model VCE    : Robust

Expression   : Pr(old), predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1569475   .0086492    18.15   0.000     .1399954    .1738996
          2  |   .1774516   .0156662    11.33   0.000     .1467464    .2081568
          3  |   .1533242   .0087902    17.44   0.000     .1360957    .1705528
          4  |   .1896612   .0158831    11.94   0.000     .1585308    .2207915
------------------------------------------------------------------------------

. estimates store old

. 
. *Version 1 horizontally
. coefplot (idtecon, msymb(O) mcolor(red*1.5) ciopts(lcolor(red*1.5)))  ///
>    idtfamily, msymb(X) mcolor(blue) ciopts(lcolor(blue))  ///
>    coeflabels(1._at = "May 2020, West" ///
>               2._at = "May 2020, East" ///
>                           3._at = "November 2020, West" ///
>                           4._at = "November 2020, East", labsize(3.5)) ///
>    title("Continuous variables", size(4.5) color(black)) ///
>    graphregion(fcolor(gs15)) ///
>    xlabel(2(.5)3, labsize(2.5)) ///
>    xline(2(.2)3, lcolor(gs12) lwidth(vthin))  ///
>    legend(order(2 "Indiv. threat economic" 4 "Indiv. threat family") rows(2)) ///
>    saving(figure2a.gph, replace)
(file figure2a.gph saved)

. 
. 
. coefplot (childrenathome, msymb(T) mcolor(green*1.1) ciopts(lcolor(green*1.1))) ///
>    (incloss, msymb(S) mcolor(black) ciopts(lcolor(black))) ///
>    (riskgroup01, msymb(+) mcolor(yellow*1.8) ciopts(lcolor(yellow*1.8)))  ///
>    old, msymb(D) mcolor(gs12) ciopts(lcolor(gs12))  ///
>    coeflabels(, nolabels) ///
>    title("Binary variables", size(4.5) color(black)) ///
>    graphregion(fcolor(gs15)) ///
>    xlabel(0(.1).5, labsize(2.5)) ///
>    xline(0(.1).5, lcolor(gs12) lwidth(vthin))  ///
>    legend(order(2 "Children at home" 4 "Income loss" 6 "Covid-19 at-risk group" ///
>                 8 "Age >70") rows(2)) ///
>    saving(figure2b.gph, replace)
(file figure2b.gph saved)

.    
. graph combine "figure2a" "figure2b"

. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table 2
. 
. bysort time east: sum sttecon [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   2,258    2759.643    3.810834    .856456          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   1,000  498.332999    3.804881   .8854729          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   2,227    2704.269    3.834394    .858304          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |     973  491.992005    3.879164   .8833133          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |       0           0


. bysort time: sum sttecon [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   3,258    3257.976    3.809924    .860863          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   3,200    3196.261    3.841286   .8622531          1          5


. 
. bysort time east: sum sttfamily [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   2,240    2738.395    3.546251   .9396018          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |     991  493.157999    3.558807   .9672946          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   2,220    2697.356    3.569247   .9403674          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |     969  490.008005    3.663728   .9625344          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |       0           0


. bysort time: sum sttfamily [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   3,231    3231.553    3.548167    .943784          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   3,189    3187.364    3.583772   .9443159          1          5


. 
. bysort time east: sum doubtstate [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   2,258    2759.643    3.507613   1.318255          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   1,000  498.332999    3.725783   1.397455          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   2,226    2702.962    3.476376   1.358789          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |     974  492.879005    3.686932   1.342688          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |       0           0


. bysort time: sum doubtstate [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   3,258    3257.976    3.540983   1.332838          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   3,200    3195.841    3.508849   1.358297          1          7


. 
. bysort time east: sum protransfer [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   2,243    2739.268    4.050865   2.703425          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |     989  492.366999    4.491745   2.722839          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   2,214    2686.558    3.966043   2.737881          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |     964  487.395005    4.321915   2.768971          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |       0           0


. bysort time: sum protransfer [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   3,232    3231.635    4.118037   2.710725          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   3,178    3173.953    4.020691   2.745365          0         10


. 
. bysort time east: tab leftrightcat [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left | 494.174999       17.96       17.96
      2. mid |  1,279.148       46.49       64.45
    3. right |    325.879       11.84       76.30
4. DK and NR | 652.062999       23.70      100.00
-------------+-----------------------------------
       Total |  2,751.265      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |    103.046       20.76       20.76
      2. mid |230.7149992       46.48       67.24
    3. right |     66.998       13.50       80.74
4. DK and NR | 95.6249999       19.26      100.00
-------------+-----------------------------------
       Total | 496.383999      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |    470.022       17.41       17.41
      2. mid |   1,231.35       45.62       63.04
    3. right | 358.695999       13.29       76.33
4. DK and NR | 638.964999       23.67      100.00
-------------+-----------------------------------
       Total |  2,699.033      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |94.50400123       19.21       19.21
      2. mid |227.6960021       46.29       65.50
    3. right | 71.9330007       14.62       80.13
4. DK and NR | 97.7480008       19.87      100.00
-------------+-----------------------------------
       Total | 491.881005      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations


. bysort time: tab leftrightcat [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left | 597.220999       18.39       18.39
      2. mid |  1,509.863       46.49       64.88
    3. right |    392.877       12.10       76.98
4. DK and NR | 747.687999       23.02      100.00
-------------+-----------------------------------
       Total |  3,247.649      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left | 564.526002       17.69       17.69
      2. mid |  1,459.046       45.73       63.42
    3. right |    430.629       13.50       76.91
4. DK and NR | 736.712999       23.09      100.00
-------------+-----------------------------------
       Total |  3,190.914      100.00


. 
. bysort time: tab east [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

       east |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  2,759.643       84.70       84.70
          1 | 498.332999       15.30      100.00
------------+-----------------------------------
      Total |  3,257.976      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

       east |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  2,704.269       84.58       84.58
          1 | 492.879005       15.42      100.00
------------+-----------------------------------
      Total |  3,197.148      100.00


. 
. bysort time east: sum gentrust [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   2,248    2747.689    4.151285   2.519669          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |     997  497.169999    4.135388   2.564668          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   2,221        2696    4.233903   2.580747          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |     973  492.384005    4.337495   2.584645          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |       0           0


. bysort time: sum gentrust [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   3,245    3244.859    4.148849   2.526338          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   3,194    3188.384    4.249901   2.581329          0         10


. 
. bysort time east: tab female [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,354.578       49.09       49.09
          1 |  1,405.065       50.91      100.00
------------+-----------------------------------
      Total |  2,759.643      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 | 244.785998       49.12       49.12
          1 |    253.547       50.88      100.00
------------+-----------------------------------
      Total | 498.332999      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,324.156       48.97       48.97
          1 |  1,380.113       51.03      100.00
------------+-----------------------------------
      Total |  2,704.269      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 | 239.944003       48.68       48.68
          1 | 252.935002       51.32      100.00
------------+-----------------------------------
      Total | 492.879005      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations


. bysort time: tab female [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,599.364       49.09       49.09
          1 |  1,658.612       50.91      100.00
------------+-----------------------------------
      Total |  3,257.976      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    1,564.1       48.92       48.92
          1 |  1,633.048       51.08      100.00
------------+-----------------------------------
      Total |  3,197.148      100.00


. 
. bysort time east: sum educyr [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   2,241     2737.88    13.57277   2.813111          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |     987  491.829999    13.87939   2.366146          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   2,213    2685.175    13.63528   2.745723          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |     971  491.528005    13.95482   2.326943          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |       0           0


. bysort time: sum educyr [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   3,228     3229.71    13.61946   2.751671          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   3,184    3176.703    13.68472   2.687411          7         22


. 
. bysort time east: sum kr_inz_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   2,213    2701.831    .5479565   .5943786          0       8.03

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |     991  493.625999    .2318989   .5189215          0       6.58

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   2,192    2659.595    14.52052   5.396691       1.83      38.64

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |     962  486.069005    8.914234   6.024722        1.5      36.57

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |       0           0


. bysort time: sum kr_inz_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   3,204    3195.457    .4991328   .5943816          0       8.03

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   3,154    3145.664    13.65423   5.859408        1.5      38.64


. 
. bysort time east: sum kr_tod_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   2,213    2701.831    1.136842   1.092035          0   10.04373

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |     991  493.625999    .4823094   .6878416          0   4.825561

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   2,192    2659.595    1.670292    1.20086          0   20.26483

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |     962  486.069005    .9870068   .9932571          0   5.420912

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |       0           0


. bysort time: sum kr_tod_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   3,204    3195.457    1.035731    1.06639          0   10.04373

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   3,154    3145.664    1.564711   1.196835          0   20.26483


. 
. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table S1 (supplement): Opposition against confinement measures
. bysort time east: reg openkitas5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,758.68499928713)

Linear regression                               Number of obs     =      2,257
                                                F(0, 2256)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2543

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.104903   .0284334   109.20   0.000     3.049145    3.160662
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.8349986970425)

Linear regression                               Number of obs     =        999
                                                F(0, 998)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2907

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.524101   .0435683    80.89   0.000     3.438605    3.609597
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,697.0439953804)

Linear regression                               Number of obs     =      2,221
                                                F(0, 2220)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2855

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    3.05854   .0293173   104.33   0.000     3.001048    3.116033
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.3960047662258)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2316

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.396273   .0421193    80.63   0.000     3.313617    3.478928
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg openkitas5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,256.51999798417)

Linear regression                               Number of obs     =      3,256
                                                F(0, 3255)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2688

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.168987   .0250825   126.34   0.000     3.119808    3.218167
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,189.44000014663)

Linear regression                               Number of obs     =      3,194
                                                F(0, 3193)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      1.283

------------------------------------------------------------------------------
             |               Robust
  openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.110681   .0257123   120.98   0.000     3.060266    3.161095
------------------------------------------------------------------------------

. 
. bysort time east: reg openschools5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,751.88199932873)

Linear regression                               Number of obs     =      2,251
                                                F(0, 2250)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2592

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.287021   .0283018   116.14   0.000      3.23152    3.342521
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 495.6799986958504)

Linear regression                               Number of obs     =        996
                                                F(0, 995)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2782

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.661875    .043261    84.65   0.000     3.576981    3.746768
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,698.69299541414)

Linear regression                               Number of obs     =      2,221
                                                F(0, 2220)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2881

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.043815   .0295643   102.96   0.000     2.985839    3.101792
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.4140047729015)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2027

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.431998   .0408477    84.02   0.000     3.351839    3.512158
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg openschools5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,247.56199802458)

Linear regression                               Number of obs     =      3,247
                                                F(0, 3246)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2691

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.344235   .0249317   134.14   0.000     3.295352    3.393119
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,191.10700018704)

Linear regression                               Number of obs     =      3,194
                                                F(0, 3193)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2828

------------------------------------------------------------------------------
             |               Robust
openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.103715   .0259039   119.82   0.000     3.052925    3.154505
------------------------------------------------------------------------------

. 
. bysort time east: reg openrestau5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,755.87099927664)

Linear regression                               Number of obs     =      2,255
                                                F(0, 2254)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3004

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.211776   .0292553   109.78   0.000     3.154406    3.269146
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.9389986991882)

Linear regression                               Number of obs     =        999
                                                F(0, 998)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3006

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.568447    .043976    81.15   0.000     3.482151    3.654743
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,694.45799541473)

Linear regression                               Number of obs     =      2,220
                                                F(0, 2219)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4387

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.947472   .0327689    89.95   0.000     2.883211    3.011733
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.3960047662258)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4224

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.233034    .048289    66.95   0.000     3.138271    3.327797
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg openrestau5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,253.80999797583)

Linear regression                               Number of obs     =      3,254
                                                F(0, 3253)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3066

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.266358   .0257344   126.93   0.000     3.215901    3.316816
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,186.85400018096)

Linear regression                               Number of obs     =      3,193
                                                F(0, 3192)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4397

------------------------------------------------------------------------------
             |               Robust
 openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.991594   .0287383   104.10   0.000     2.935247    3.047941
------------------------------------------------------------------------------

. 
. bysort time east: reg opencontact5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,756.96599924564)

Linear regression                               Number of obs     =      2,256
                                                F(0, 2255)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2643

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.198347   .0287769   111.14   0.000     3.141915    3.254779
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.8749986886978)

Linear regression                               Number of obs     =        999
                                                F(0, 998)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2774

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.554153   .0431706    82.33   0.000     3.469438    3.638869
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,696.61099553108)

Linear regression                               Number of obs     =      2,221
                                                F(0, 2220)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4059

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.590682   .0320187    80.91   0.000     2.527893    2.653472
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 489.6570047438145)

Linear regression                               Number of obs     =        970
                                                F(0, 969)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4338

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.000437    .048463    61.91   0.000     2.905332    3.095542
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg opencontact5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,254.84099793434)

Linear regression                               Number of obs     =      3,255
                                                F(0, 3254)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2726

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.252773    .025313   128.50   0.000     3.203142    3.302404
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,186.2680002749)

Linear regression                               Number of obs     =      3,191
                                                F(0, 3190)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4178

------------------------------------------------------------------------------
             |               Robust
opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.653652   .0281794    94.17   0.000     2.598401    2.708904
------------------------------------------------------------------------------

. 
. bysort time east: reg openborders5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,747.52899926901)

Linear regression                               Number of obs     =      2,247
                                                F(0, 2246)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4045

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.047968   .0319006    95.55   0.000      2.98541    3.110526
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4005

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.128599   .0471003    66.42   0.000     3.036172    3.221026
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,699.74399535358)

Linear regression                               Number of obs     =      2,223
                                                F(0, 2222)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.5139

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.737206   .0345139    79.31   0.000     2.669523    2.804889
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.4660047888756)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      1.493

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.748173   .0505574    54.36   0.000     2.648959    2.847388
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg openborders5 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,245.861997962)

Linear regression                               Number of obs     =      3,247
                                                F(0, 3246)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4041

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.060347   .0279554   109.47   0.000     3.005535    3.115159
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,192.21000014246)

Linear regression                               Number of obs     =      3,196
                                                F(0, 3195)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.5106

------------------------------------------------------------------------------
             |               Robust
openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.738898   .0302111    90.66   0.000     2.679663    2.798133
------------------------------------------------------------------------------

. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table S2 (supplement): Descriptive analysis: self-interest indicators (figure 3 from main article)
. 
. bysort time east: reg idtecon [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,758.0779992938)

Linear regression                               Number of obs     =      2,256
                                                F(0, 2255)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1751

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.357223   .0267272    88.20   0.000     2.304811    2.409636
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 496.0119987130165)

Linear regression                               Number of obs     =        997
                                                F(0, 996)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1421

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.320482   .0377637    61.45   0.000     2.246377    2.394588
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,702.03499531746)

Linear regression                               Number of obs     =      2,225
                                                F(0, 2224)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2187

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.535678   .0277782    91.28   0.000     2.481205    2.590152
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 491.9920047521591)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2332

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.565025   .0416451    61.59   0.000     2.483301     2.64675
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg idtecon [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,254.08999800682)

Linear regression                               Number of obs     =      3,253
                                                F(0, 3252)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1701

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.351623   .0233719   100.62   0.000     2.305798    2.397448
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,194.02700006962)

Linear regression                               Number of obs     =      3,198
                                                F(0, 3197)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2208

------------------------------------------------------------------------------
             |               Robust
     idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.540199   .0243565   104.29   0.000     2.492443    2.587955
------------------------------------------------------------------------------

. 
. bysort time east: reg incloss [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,745.04699918628)

Linear regression                               Number of obs     =      2,247
                                                F(0, 2246)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36526

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1584694   .0081467    19.45   0.000     .1424935    .1744453
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.9049986898899)

Linear regression                               Number of obs     =        999
                                                F(0, 998)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      .3618

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1546962   .0113531    13.63   0.000     .1324175    .1769748
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,704.26899540424)

Linear regression                               Number of obs     =      2,227
                                                F(0, 2226)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .34691

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1398518   .0078464    17.82   0.000     .1244648    .1552389
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.8790047764778)

Linear regression                               Number of obs     =        974
                                                F(0, 973)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =       .329

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1233467   .0107289    11.50   0.000     .1022922    .1444012
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg incloss [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,242.95199787617)

Linear regression                               Number of obs     =      3,246
                                                F(0, 3245)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36469

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1578901   .0071123    22.20   0.000      .143945    .1718351
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,197.14800018072)

Linear regression                               Number of obs     =      3,201
                                                F(0, 3200)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .34423

------------------------------------------------------------------------------
             |               Robust
     incloss |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1373074     .00684    20.07   0.000     .1238962    .1507186
------------------------------------------------------------------------------

. 
. bysort time east: reg idtfamily [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,738.30999937654)

Linear regression                               Number of obs     =      2,240
                                                F(0, 2239)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.0967

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.053636   .0247818    82.87   0.000     2.005038    2.102234
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 494.6379987895489)

Linear regression                               Number of obs     =        994
                                                F(0, 993)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.0789

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.088449   .0363803    57.41   0.000     2.017057     2.15984
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,688.89399543405)

Linear regression                               Number of obs     =      2,215
                                                F(0, 2214)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1719

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.281753   .0267552    85.28   0.000     2.229285    2.334221
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 489.1670046746731)

Linear regression                               Number of obs     =        967
                                                F(0, 966)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1559

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.427942   .0394027    61.62   0.000     2.350617    2.505267
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg idtfamily [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,232.94799816608)

Linear regression                               Number of obs     =      3,234
                                                F(0, 3233)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      1.094

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.058962    .021716    94.81   0.000     2.016384    2.101541
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,178.06100010872)

Linear regression                               Number of obs     =      3,182
                                                F(0, 3181)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1705

------------------------------------------------------------------------------
             |               Robust
   idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   2.304254   .0234483    98.27   0.000     2.258279     2.35023
------------------------------------------------------------------------------

. 
. bysort time east: reg childrenathome [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,730.91399943829)

Linear regression                               Number of obs     =      2,236
                                                F(0, 2235)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .39568

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1942057    .008702    22.32   0.000     .1771407    .2112706
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 492.7909988164902)

Linear regression                               Number of obs     =        990
                                                F(0, 989)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .38212

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1773064   .0119016    14.90   0.000     .1539512    .2006616
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,656.88499568403)

Linear regression                               Number of obs     =      2,190
                                                F(0, 2189)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .38729

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1836493   .0087098    21.09   0.000     .1665689    .2007297
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 483.3150045573711)

Linear regression                               Number of obs     =        959
                                                F(0, 958)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .37443

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .168416   .0118448    14.22   0.000     .1451714    .1916607
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg childrenathome [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,223.70499825478)

Linear regression                               Number of obs     =      3,226
                                                F(0, 3225)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .39364

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1916224   .0075923    25.24   0.000     .1767362    .2065085
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,140.2000002414)

Linear regression                               Number of obs     =      3,149
                                                F(0, 3148)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .38533

------------------------------------------------------------------------------
             |               Robust
childrenat~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1813047   .0075915    23.88   0.000       .16642    .1961894
------------------------------------------------------------------------------

. 
. bysort time east: reg riskgroup01 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,637.53699925542)

Linear regression                               Number of obs     =      2,168
                                                F(0, 2167)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .48446

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .375881   .0112061    33.54   0.000     .3539052    .3978568
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 476.28299883008)

Linear regression                               Number of obs     =        956
                                                F(0, 955)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .49231

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4112198   .0172857    23.79   0.000     .3772975    .4451421
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,586.94199559093)

Linear regression                               Number of obs     =      2,140
                                                F(0, 2139)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .48924

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .3962872   .0114022    34.76   0.000     .3739266    .4186478
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 477.6510046720505)

Linear regression                               Number of obs     =        945
                                                F(0, 944)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .49368

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4191261   .0172849    24.25   0.000     .3852049    .4530473
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg riskgroup01 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,113.8199980855)

Linear regression                               Number of obs     =      3,124
                                                F(0, 3123)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .48578

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .3812863   .0098592    38.67   0.000     .3619552    .4006174
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,064.59300026298)

Linear regression                               Number of obs     =      3,085
                                                F(0, 3084)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .48995

------------------------------------------------------------------------------
             |               Robust
 riskgroup01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .3998469   .0099972    40.00   0.000      .380245    .4194488
------------------------------------------------------------------------------

. 
. bysort time east: reg old [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,759.64299929142)

Linear regression                               Number of obs     =      2,258
                                                F(0, 2257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36383

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1569475   .0086504    18.14   0.000     .1399838    .1739111
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .38224

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1774516   .0156728    11.32   0.000     .1466962    .2082071
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,704.26899540424)

Linear regression                               Number of obs     =      2,227
                                                F(0, 2226)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36038

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1533242   .0087915    17.44   0.000     .1360838    .1705647
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.8790047764778)

Linear regression                               Number of obs     =        974
                                                F(0, 973)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .39223

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1896612     .01589    11.94   0.000     .1584785    .2208438
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg old [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36674

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1600837   .0077132    20.75   0.000     .1449604    .1752071
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,197.14800018072)

Linear regression                               Number of obs     =      3,201
                                                F(0, 3200)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36566

------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .158926   .0078351    20.28   0.000     .1435638    .1742883
------------------------------------------------------------------------------

. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table S3 (supplement): Descriptive analysis: Sociotropic threat, political orientations, infection rates and control variables 
. 
. bysort time east: reg sttecon [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,759.64299929142)

Linear regression                               Number of obs     =      2,258
                                                F(0, 2257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .85646

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.810834   .0196726   193.71   0.000     3.772256    3.849413
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .88547

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.804881   .0298019   127.67   0.000       3.7464    3.863363
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,704.26899540424)

Linear regression                               Number of obs     =      2,227
                                                F(0, 2226)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      .8583

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.834394   .0194661   196.98   0.000     3.796221    3.872568
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 491.9920047521591)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .88331

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.879164   .0303749   127.71   0.000     3.819556    3.938772
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg sttecon [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .86086

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.809924   .0172739   220.56   0.000     3.776055    3.843793
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,196.2610001564)

Linear regression                               Number of obs     =      3,200
                                                F(0, 3199)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .86225

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.841286   .0171199   224.38   0.000     3.807719    3.874853
------------------------------------------------------------------------------

. 
. bysort time east: reg sttfamily [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,738.39499929547)

Linear regression                               Number of obs     =      2,240
                                                F(0, 2239)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      .9396

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.546251   .0215044   164.91   0.000     3.504081    3.588422
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 493.1579987406731)

Linear regression                               Number of obs     =        991
                                                F(0, 990)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .96729

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.558807   .0327976   108.51   0.000     3.494446    3.623168
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,697.35599538684)

Linear regression                               Number of obs     =      2,220
                                                F(0, 2219)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .94037

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.569247   .0214152   166.67   0.000     3.527251    3.611243
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 490.0080046951771)

Linear regression                               Number of obs     =        969
                                                F(0, 968)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .96253

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.663728   .0331672   110.46   0.000      3.59864    3.728816
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg sttfamily [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,231.55299803615)

Linear regression                               Number of obs     =      3,231
                                                F(0, 3230)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .94378

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.548167   .0188968   187.77   0.000     3.511117    3.585218
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,187.36400008202)

Linear regression                               Number of obs     =      3,189
                                                F(0, 3188)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .94432

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.583772   .0188314   190.31   0.000     3.546849    3.620695
------------------------------------------------------------------------------

. 
. bysort time east: reg doubtstate [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,759.64299929142)

Linear regression                               Number of obs     =      2,258
                                                F(0, 2257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3183

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.507613   .0298085   117.67   0.000     3.449158    3.566067
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3975

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.725783    .047439    78.54   0.000     3.632691    3.818874
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,702.96199536324)

Linear regression                               Number of obs     =      2,226
                                                F(0, 2225)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3588

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.476376   .0311322   111.67   0.000     3.415324    3.537427
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.8790047764778)

Linear regression                               Number of obs     =        974
                                                F(0, 973)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3427

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.686932    .045711    80.66   0.000     3.597229    3.776636
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg doubtstate [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3328

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.540983   .0262919   134.68   0.000     3.489433    3.592534
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,195.84100013971)

Linear regression                               Number of obs     =      3,200
                                                F(0, 3199)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3583

------------------------------------------------------------------------------
             |               Robust
  doubtstate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.508849   .0272795   128.63   0.000     3.455362    3.562336
------------------------------------------------------------------------------

. 
. bysort time east: reg protransfer [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,739.26799911261)

Linear regression                               Number of obs     =      2,243
                                                F(0, 2242)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7034

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.050865   .0623895    64.93   0.000     3.928518    4.173212
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 492.36699873209)

Linear regression                               Number of obs     =        989
                                                F(0, 988)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7228

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.491745   .0925495    48.53   0.000     4.310129    4.673361
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,686.55799567699)

Linear regression                               Number of obs     =      2,214
                                                F(0, 2213)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7379

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   3.966043   .0623678    63.59   0.000     3.843738    4.088349
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 487.3950047194958)

Linear regression                               Number of obs     =        964
                                                F(0, 963)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      2.769

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.321915   .0949606    45.51   0.000     4.135562    4.508269
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg protransfer [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,231.6349978447)

Linear regression                               Number of obs     =      3,232
                                                F(0, 3231)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7107

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.118037   .0547717    75.19   0.000     4.010646    4.225428
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,173.95300039649)

Linear regression                               Number of obs     =      3,178
                                                F(0, 3177)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7454

------------------------------------------------------------------------------
             |               Robust
 protransfer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.020691    .054786    73.39   0.000     3.913272    4.128111
------------------------------------------------------------------------------

. 
. mlogit leftrightcat [pweight=persweight] if time==0 & east==0 

Iteration 0:   log pseudolikelihood = -3462.0719  
Iteration 1:   log pseudolikelihood = -3462.0719  

Multinomial logistic regression                 Number of obs     =      2,253
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -3462.0719               Pseudo R2         =     0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.9510598   .0639969   -14.86   0.000    -1.076491   -.8256282
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.367423   .0738595   -18.51   0.000    -1.512185   -1.222661
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.6738083   .0568055   -11.86   0.000     -.785145   -.5624716
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =      2,253
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1796174   .0088284    20.35   0.000      .162314    .1969208
          2  |   .4649309   .0113755    40.87   0.000     .4426353    .4872264
          3  |    .118447   .0073165    16.19   0.000     .1041069     .132787
          4  |   .2370048   .0095367    24.85   0.000     .2183131    .2556964
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==0 & east==1 

Iteration 0:   log pseudolikelihood = -630.43474  
Iteration 1:   log pseudolikelihood = -630.43474  

Multinomial logistic regression                 Number of obs     =        997
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -630.43474               Pseudo R2         =    -0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.8060077   .0909909    -8.86   0.000    -.9843466   -.6276688
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |   -1.23652   .1030276   -12.00   0.000    -1.438451    -1.03459
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.8807489   .0912472    -9.65   0.000     -1.05959   -.7019076
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =        997
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .2075933   .0140231    14.80   0.000     .1801086    .2350781
          2  |   .4647914   .0168682    27.55   0.000     .4317302    .4978525
          3  |   .1349721   .0113864    11.85   0.000     .1126552    .1572891
          4  |   .1926432   .0133001    14.48   0.000     .1665754     .218711
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==1 & east==0 

Iteration 0:   log pseudolikelihood = -3432.4099  
Iteration 1:   log pseudolikelihood = -3432.4099  

Multinomial logistic regression                 Number of obs     =      2,223
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -3432.4099               Pseudo R2         =     0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.9630869   .0649705   -14.82   0.000    -1.090427   -.8357471
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.233391   .0707443   -17.43   0.000    -1.372047   -1.094735
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.6560167   .0576419   -11.38   0.000    -.7689927   -.5430407
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =      2,223
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1741446   .0087334    19.94   0.000     .1570275    .1912617
          2  |   .4562189   .0114157    39.96   0.000     .4338446    .4785933
          3  |    .132898   .0076833    17.30   0.000     .1178391    .1479569
          4  |   .2367385   .0096439    24.55   0.000     .2178369    .2556401
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==1 & east==1 

Iteration 0:   log pseudolikelihood = -627.50739  
Iteration 1:   log pseudolikelihood = -627.50739  

Multinomial logistic regression                 Number of obs     =        972
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -627.50739               Pseudo R2         =     0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.8793692   .0920067    -9.56   0.000    -1.059699   -.6990394
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.152276   .1036782   -11.11   0.000    -1.355482   -.9490707
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.8456187   .0925175    -9.14   0.000     -1.02695   -.6642876
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =        972
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1921278   .0133992    14.34   0.000     .1658659    .2183897
          2  |   .4629087   .0170069    27.22   0.000     .4295758    .4962416
          3  |   .1462407   .0122622    11.93   0.000     .1222072    .1702741
          4  |   .1987229   .0138272    14.37   0.000     .1716221    .2258237
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==0 

Iteration 0:   log pseudolikelihood = -4095.7174  
Iteration 1:   log pseudolikelihood = -4095.7174  

Multinomial logistic regression                 Number of obs     =      3,250
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -4095.7174               Pseudo R2         =     0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |   -.927487   .0554258   -16.73   0.000     -1.03612   -.8188544
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.346278    .063883   -21.07   0.000    -1.471486   -1.221069
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.7027884   .0505807   -13.89   0.000    -.8019248    -.603652
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =      3,250
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1838933   .0077809    23.63   0.000      .168643    .1991437
          2  |   .4649095   .0099749    46.61   0.000     .4453591      .48446
          3  |   .1209727   .0064379    18.79   0.000     .1083547    .1335908
          4  |   .2302244   .0083305    27.64   0.000     .2138969    .2465519
------------------------------------------------------------------------------

. mlogit leftrightcat [pweight=persweight] if time==1 

Iteration 0:   log pseudolikelihood = -4061.9333  
Iteration 1:   log pseudolikelihood = -4061.9333  

Multinomial logistic regression                 Number of obs     =      3,195
                                                Wald chi2(0)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -4061.9333               Pseudo R2         =    -0.0000

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
       _cons |  -.9495516   .0563559   -16.85   0.000    -1.060007   -.8390961
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
       _cons |  -1.220291   .0615174   -19.84   0.000    -1.340863   -1.099719
-------------+----------------------------------------------------------------
4__DK_and_NR |
       _cons |  -.6833397   .0511819   -13.35   0.000    -.7836543   -.5830251
------------------------------------------------------------------------------

. margins
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.
Warning: prediction constant over observations.

Predictive margins                              Number of obs     =      3,195
Model VCE    : Robust

1._predict   : Pr(leftrightcat==1__left), predict(pr outcome(1))
2._predict   : Pr(leftrightcat==2__mid), predict(pr outcome(2))
3._predict   : Pr(leftrightcat==3__right), predict(pr outcome(3))
4._predict   : Pr(leftrightcat==4__DK_and_NR), predict(pr outcome(4))

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _predict |
          1  |   .1769167   .0076699    23.07   0.000      .161884    .1919494
          2  |   .4572502   .0100043    45.71   0.000     .4376422    .4768582
          3  |   .1349547   .0067686    19.94   0.000     .1216885     .148221
          4  |   .2308784   .0084305    27.39   0.000     .2143549    .2474018
------------------------------------------------------------------------------

. 
. bysort time: reg east [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =        .36

------------------------------------------------------------------------------
             |               Robust
        east |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1529578   .0052795    28.97   0.000     .1426064    .1633093
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,197.14800018072)

Linear regression                               Number of obs     =      3,201
                                                F(0, 3200)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .36116

------------------------------------------------------------------------------
             |               Robust
        east |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1541621   .0053552    28.79   0.000     .1436622     .164662
------------------------------------------------------------------------------

. 
. bysort time east: reg gentrust [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,747.68899920583)

Linear regression                               Number of obs     =      2,248
                                                F(0, 2247)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5197

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.151285   .0576845    71.97   0.000     4.038164    4.264405
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 497.1699986755848)

Linear regression                               Number of obs     =        997
                                                F(0, 996)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5647

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.135388   .0877746    47.11   0.000     3.963144    4.307633
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,695.99999535084)

Linear regression                               Number of obs     =      2,221
                                                F(0, 2220)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5807

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.233903   .0587405    72.08   0.000     4.118711    4.349095
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.3840047717094)

Linear regression                               Number of obs     =        973
                                                F(0, 972)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5846

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.337495    .089389    48.52   0.000     4.162077    4.512912
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg gentrust [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,244.85899788141)

Linear regression                               Number of obs     =      3,245
                                                F(0, 3244)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5263

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.148849   .0506606    81.89   0.000     4.049519    4.248179
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,188.38400012255)

Linear regression                               Number of obs     =      3,194
                                                F(0, 3193)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.5813

------------------------------------------------------------------------------
             |               Robust
    gentrust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   4.249901   .0515504    82.44   0.000     4.148825    4.350976
------------------------------------------------------------------------------

. 
. bysort time east: reg female [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,759.64299929142)

Linear regression                               Number of obs     =      2,258
                                                F(0, 2257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .50003

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5091474   .0113771    44.75   0.000     .4868366    .5314581
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 498.3329986929893)

Linear regression                               Number of obs     =      1,000
                                                F(0, 999)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .50017

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5087903   .0169104    30.09   0.000     .4756063    .5419743
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,704.26899540424)

Linear regression                               Number of obs     =      2,227
                                                F(0, 2226)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .50001

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .510346   .0114347    44.63   0.000     .4879223    .5327698
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 492.8790047764778)

Linear regression                               Number of obs     =        974
                                                F(0, 973)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .50008

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5131787   .0170708    30.06   0.000      .479679    .5466784
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg female [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,257.97599798441)

Linear regression                               Number of obs     =      3,258
                                                F(0, 3257)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .49999

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5090928   .0099771    51.03   0.000     .4895307    .5286549
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,197.14800018072)

Linear regression                               Number of obs     =      3,201
                                                F(0, 3200)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .49996

------------------------------------------------------------------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5107827   .0100226    50.96   0.000     .4911313    .5304342
------------------------------------------------------------------------------

. 
. bysort time east: reg educyr [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,737.87999927998)

Linear regression                               Number of obs     =      2,241
                                                F(0, 2240)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.8131

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.57277   .0639601   212.21   0.000     13.44734    13.69819
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 491.8299987018108)

Linear regression                               Number of obs     =        987
                                                F(0, 986)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.3661

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.87939   .0808316   171.71   0.000     13.72077    14.03801
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,685.17499524355)

Linear regression                               Number of obs     =      2,213
                                                F(0, 2212)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7457

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.63528   .0629437   216.63   0.000     13.51184    13.75871
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 491.5280047953129)

Linear regression                               Number of obs     =        971
                                                F(0, 970)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.3269

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.95482   .0800725   174.28   0.000     13.79769    14.11196
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg educyr [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,229.70999798179)

Linear regression                               Number of obs     =      3,228
                                                F(0, 3227)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.7517

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.61946   .0556235   244.85   0.000      13.5104    13.72852
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,176.70300003886)

Linear regression                               Number of obs     =      3,184
                                                F(0, 3183)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     2.6874

------------------------------------------------------------------------------
             |               Robust
      educyr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.68472    .054654   250.39   0.000     13.57756    13.79188
------------------------------------------------------------------------------

. 
. bysort time east: reg kr_inz_rate10 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,701.83099935949)

Linear regression                               Number of obs     =      2,213
                                                F(0, 2212)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .59438

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5479565    .013914    39.38   0.000     .5206706    .5752425
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 493.6259987652302)

Linear regression                               Number of obs     =        991
                                                F(0, 990)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .51892

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2318989   .0162351    14.28   0.000     .2000398    .2637581
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,659.59499521554)

Linear regression                               Number of obs     =      2,192
                                                F(0, 2191)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     5.3967

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   14.52052   .1248849   116.27   0.000     14.27561    14.76542
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 486.069004714489)

Linear regression                               Number of obs     =        962
                                                F(0, 961)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     6.0247

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   8.914234   .2020679    44.12   0.000     8.517689    9.310779
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg kr_inz_rate10 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,195.45699812472)

Linear regression                               Number of obs     =      3,204
                                                F(0, 3203)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .59438

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4991328   .0121498    41.08   0.000     .4753106    .5229549
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,145.66399993002)

Linear regression                               Number of obs     =      3,154
                                                F(0, 3153)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     5.8594

------------------------------------------------------------------------------
             |               Robust
kr_inz_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   13.65423   .1140681   119.70   0.000     13.43058    13.87789
------------------------------------------------------------------------------

. 
. bysort time east: reg kr_tod_rate10 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0
(sum of wgt is 2,701.83099935949)

Linear regression                               Number of obs     =      2,213
                                                F(0, 2212)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =      1.092

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   1.136842   .0252022    45.11   0.000     1.087419    1.186264
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1
(sum of wgt is 493.6259987652302)

Linear regression                               Number of obs     =        991
                                                F(0, 990)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .68784

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4823094   .0223521    21.58   0.000     .4384466    .5261723
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0
(sum of wgt is 2,659.59499521554)

Linear regression                               Number of obs     =      2,192
                                                F(0, 2191)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2009

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   1.670292     .02745    60.85   0.000     1.616462    1.724123
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1
(sum of wgt is 486.069004714489)

Linear regression                               Number of obs     =        962
                                                F(0, 961)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .99326

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .9870068   .0336198    29.36   0.000       .92103    1.052984
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations

. bysort time: reg kr_tod_rate10 [pweight=persweight] 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020
(sum of wgt is 3,195.45699812472)

Linear regression                               Number of obs     =      3,204
                                                F(0, 3203)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.0664

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   1.035731   .0218883    47.32   0.000     .9928147    1.078648
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020
(sum of wgt is 3,145.66399993002)

Linear regression                               Number of obs     =      3,154
                                                F(0, 3153)        =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     1.1968

------------------------------------------------------------------------------
             |               Robust
kr_tod_ra~10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   1.564711   .0241055    64.91   0.000     1.517447    1.611975
------------------------------------------------------------------------------

. 
. 
end of do-file

. do "robustness analysis regressions.do"

. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *
. * Diehl/Wolter: “ATTITUDES ABOUT CONTAINMENT MEASURES DURING
. *                THE 2020/2021 CORONAVIRUS PANDEMIC: 
. *                SELF-INTEREST, OR BROADER POLITICAL ORIENTATIONS?"
. *
. * Do-File for regression analysis
. *
. * This version: 20210602
. *
. * Author: Felix Wolter, felix.wolter@uni-konstanz.de
. *
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. 
. *Final full models, figure 3 and table S4 (supplement)
. *Check whether two-dimensional trust indices (political vs. technocratic)
. * have different effects.
. 
. egen trustpoliti = rowmean(trustbundes* trustland trustparties)
(42 missing values generated)

. egen trusttechnocr = rowmean(trusthealthsys trustpolice trustscience)
(43 missing values generated)

. 
. gen doubtpoliti = 8-trustpoliti
(42 missing values generated)

. gen doubttechnocr = 8-trusttechnocr
(43 missing values generated)

. 
. 
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtpoliti c.doubttechnocr c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==0 //no different effects
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(20, 2914)       =      17.76
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1154
                                                Root MSE          =     .95314

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0401322   .0236948    -1.69   0.090    -.0865924    .0063279
       1.incloss |   .0406697   .0619557     0.66   0.512    -.0808117    .1621511
       idtfamily |  -.0159061   .0237261    -0.67   0.503    -.0624276    .0306155
1.childrenathome |  -.0113424   .0512475    -0.22   0.825    -.1118274    .0891425
   1.riskgroup01 |  -.3519499   .0432281    -8.14   0.000    -.4367107   -.2671891
           1.old |  -.0725836     .06097    -1.19   0.234    -.1921322    .0469651
         sttecon |    .048747   .0307181     1.59   0.113    -.0114843    .1089783
       sttfamily |    .085693    .027728     3.09   0.002     .0313246    .1400614
     doubtpoliti |   .0900627   .0200229     4.50   0.000     .0508024    .1293231
   doubttechnocr |   .0961407   .0218466     4.40   0.000     .0533044    .1389769
     protransfer |  -.0132472    .008191    -1.62   0.106    -.0293079    .0028136
                 |
    leftrightcat |
        1. left  |  -.1262277   .0543878    -2.32   0.020    -.2328701   -.0195853
       3. right  |  -.0136091   .0699277    -0.19   0.846    -.1507218    .1235035
   4. DK and NR  |  -.0551539   .0532949    -1.03   0.301    -.1596535    .0493456
                 |
          1.east |   .3042235   .0437235     6.96   0.000     .2184915    .3899555
        gentrust |   .0384124   .0093246     4.12   0.000     .0201289    .0566959
        1.female |  -.0793338   .0409225    -1.94   0.053    -.1595738    .0009062
          educyr |   .0016014   .0078711     0.20   0.839     -.013832    .0170348
   kr_inz_rate10 |  -.0105886   .0366329    -0.29   0.773    -.0824177    .0612405
   kr_tod_rate10 |    .012255   .0215295     0.57   0.569    -.0299595    .0544695
           _cons |  -.9673279   .1852721    -5.22   0.000    -1.330605   -.6040505
----------------------------------------------------------------------------------

. 
. estimates store May

. ereturn list

scalars:
               e(rank) =  21
               e(ll_0) =  -4193.165390729501
                 e(ll) =  -4013.171301839919
               e(r2_a) =  .1093587596908057
                e(rss) =  2647.272171967416
                e(mss) =  345.4496859052597
               e(rmse) =  .9531352325021877
                 e(r2) =  .1154299337897096
                  e(F) =  17.75527709896811
               e(df_r) =  2914
               e(df_m) =  20
                  e(N) =  2935

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtpoliti c.doubttechnocr c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educy.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "robust"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "pweight"
            e(vcetype) : "Robust"

matrices:
                  e(b) :  1 x 28
                  e(V) :  28 x 28
       e(V_modelbased) :  28 x 28

functions:
             e(sample)   

. 
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtpoliti c.doubttechnocr c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==1
(sum of wgt is 2,884.63099986315)

Linear regression                               Number of obs     =      2,917
                                                F(20, 2896)       =      17.13
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1299
                                                Root MSE          =     .93555

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0193202   .0225465    -0.86   0.392    -.0635289    .0248885
       1.incloss |  -.0942348   .0621874    -1.52   0.130    -.2161708    .0277012
       idtfamily |  -.0198307   .0225514    -0.88   0.379    -.0640492    .0243878
1.childrenathome |   .0529115   .0527332     1.00   0.316    -.0504869    .1563099
   1.riskgroup01 |  -.1972836   .0419638    -4.70   0.000    -.2795656   -.1150016
           1.old |   .0090457   .0579902     0.16   0.876    -.1046604    .1227519
         sttecon |   .0602591   .0305394     1.97   0.049      .000378    .1201402
       sttfamily |   .0736515   .0286578     2.57   0.010     .0174597    .1298433
     doubtpoliti |   .1630565   .0218405     7.47   0.000     .1202321     .205881
   doubttechnocr |   .0271831   .0252017     1.08   0.281    -.0222321    .0765982
     protransfer |  -.0247438   .0075238    -3.29   0.001    -.0394963   -.0099912
                 |
    leftrightcat |
        1. left  |   -.056604   .0524662    -1.08   0.281    -.1594788    .0462709
       3. right  |   .0320212   .0638752     0.50   0.616    -.0932242    .1572666
   4. DK and NR  |  -.0133225   .0519981    -0.26   0.798    -.1152794    .0886345
                 |
          1.east |   .2465356    .045699     5.39   0.000     .1569298    .3361415
        gentrust |   .0474624   .0082516     5.75   0.000     .0312828    .0636419
        1.female |  -.0945254   .0405332    -2.33   0.020    -.1740022   -.0150486
          educyr |  -.0109343   .0078422    -1.39   0.163    -.0263113    .0044426
   kr_inz_rate10 |  -.0038508   .0035922    -1.07   0.284    -.0108943    .0031927
   kr_tod_rate10 |   .0156434   .0180494     0.87   0.386    -.0197477    .0510344
           _cons |   -.945482   .1765535    -5.36   0.000    -1.291665   -.5992988
----------------------------------------------------------------------------------

.    
. estimates store November

. ereturn list

scalars:
               e(rank) =  21
               e(ll_0) =  -4137.185164759
                 e(ll) =  -3934.165594935302
               e(r2_a) =  .1239351590167405
                e(rss) =  2534.72183287333
                e(mss) =  378.5634612179952
               e(rmse) =  .9355475678354253
                 e(r2) =  .129943834194952
                  e(F) =  17.12900602158395
               e(df_r) =  2896
               e(df_m) =  20
                  e(N) =  2917

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtpoliti c.doubttechnocr c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educy.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "robust"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "pweight"
            e(vcetype) : "Robust"

matrices:
                  e(b) :  1 x 28
                  e(V) :  28 x 28
       e(V_modelbased) :  28 x 28

functions:
             e(sample)   

. 
. *20210621: Test trust coefficients against each other
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtpoliti c.doubttechnocr c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [iweight=persweight] if time==0 //no different effects

      Source |       SS           df       MS      Number of obs   =     2,911
-------------+----------------------------------   F(20, 2890)     =     18.86
       Model |  342.668433        20  17.1334217   Prob > F        =    0.0000
    Residual |   2625.9587     2,890  .908636228   R-squared       =    0.1154
-------------+----------------------------------   Adj R-squared   =    0.1094
       Total |  2968.62713     2,910  1.02014678   Root MSE        =    .95316

----------------------------------------------------------------------------------
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0401322   .0200589    -2.00   0.046    -.0794634   -.0008011
       1.incloss |   .0406697   .0520842     0.78   0.435    -.0614563    .1427957
       idtfamily |  -.0159061   .0197623    -0.80   0.421    -.0546558    .0228436
1.childrenathome |  -.0113424   .0467685    -0.24   0.808    -.1030454    .0803606
   1.riskgroup01 |  -.3519499   .0384513    -9.15   0.000    -.4273446   -.2765551
           1.old |  -.0725836   .0528831    -1.37   0.170     -.176276    .0311089
         sttecon |    .048747   .0254646     1.91   0.056    -.0011836    .0986776
       sttfamily |    .085693   .0230457     3.72   0.000     .0405054    .1308806
     doubtpoliti |   .0900627   .0165422     5.44   0.000      .057627    .1224985
   doubttechnocr |   .0961407   .0184179     5.22   0.000     .0600271    .1322543
     protransfer |  -.0132472   .0068755    -1.93   0.054    -.0267285    .0002342
                 |
    leftrightcat |
        1. left  |  -.1262277   .0489427    -2.58   0.010    -.2221939   -.0302615
       3. right  |  -.0136091   .0575394    -0.24   0.813    -.1264314    .0992132
   4. DK and NR  |  -.0551539   .0471455    -1.17   0.242    -.1475961    .0372882
                 |
          1.east |   .3042235    .051148     5.95   0.000     .2039333    .4045138
        gentrust |   .0384124   .0076266     5.04   0.000     .0234583    .0533665
        1.female |  -.0793338   .0367108    -2.16   0.031    -.1513157   -.0073519
          educyr |   .0016014   .0068917     0.23   0.816    -.0119118    .0151147
   kr_inz_rate10 |  -.0105886    .031477    -0.34   0.737    -.0723081     .051131
   kr_tod_rate10 |    .012255   .0172644     0.71   0.478    -.0215967    .0461067
           _cons |  -.9673279   .1518162    -6.37   0.000    -1.265007    -.669649
----------------------------------------------------------------------------------

. 
. estimates store May

. ereturn list

scalars:
               e(rank) =  21
               e(ll_0) =  -4159.06214332684
                 e(ll) =  -3980.539897072126
               e(r2_a) =  .1094223597551284
                e(rss) =  2625.958698211351
                e(mss) =  342.6684332284349
               e(rmse) =  .9531631090939499
                 e(r2) =  .1154299337897112
                  e(F) =  18.85860884828082
               e(df_r) =  2890
               e(df_m) =  20
                  e(N) =  2911

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtpoliti c.doubttechnocr c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educy.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "ols"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "iweight"

matrices:
                  e(b) :  1 x 28
                  e(V) :  28 x 28

functions:
             e(sample)   

. 
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtpoliti c.doubttechnocr c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [iweight=persweight] if time==1

      Source |       SS           df       MS      Number of obs   =     2,884
-------------+----------------------------------   F(20, 2863)     =     21.38
       Model |  374.362666        20  18.7181333   Prob > F        =    0.0000
    Residual |  2506.59485     2,863  .875513395   R-squared       =    0.1299
-------------+----------------------------------   Adj R-squared   =    0.1241
       Total |  2880.95751     2,883  .999291542   Root MSE        =    .93559

----------------------------------------------------------------------------------
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0193202    .020078    -0.96   0.336     -.058689    .0200486
       1.incloss |  -.0942348   .0556207    -1.69   0.090    -.2032955     .014826
       idtfamily |  -.0198307   .0193578    -1.02   0.306    -.0577873    .0181259
1.childrenathome |   .0529115   .0467604     1.13   0.258    -.0387759    .1445989
   1.riskgroup01 |  -.1972836   .0376861    -5.23   0.000    -.2711782   -.1233891
           1.old |   .0090457   .0522013     0.17   0.862    -.0933101    .1114016
         sttecon |   .0602591   .0277246     2.17   0.030     .0058968    .1146214
       sttfamily |   .0736515   .0249427     2.95   0.003     .0247439     .122559
     doubtpoliti |   .1630565   .0176135     9.26   0.000     .1285201     .197593
   doubttechnocr |   .0271831   .0194249     1.40   0.162    -.0109051    .0652712
     protransfer |  -.0247438   .0065805    -3.76   0.000    -.0376468   -.0118407
                 |
    leftrightcat |
        1. left  |   -.056604   .0490404    -1.15   0.249     -.152762    .0395541
       3. right  |   .0320212   .0545239     0.59   0.557    -.0748889    .1389314
   4. DK and NR  |  -.0133225   .0460676    -0.29   0.772    -.1036515    .0770066
                 |
          1.east |   .2465356   .0521526     4.73   0.000     .1442752     .348796
        gentrust |   .0474624   .0072232     6.57   0.000     .0332992    .0616255
        1.female |  -.0945254   .0359648    -2.63   0.009     -.165045   -.0240058
          educyr |  -.0109343   .0069676    -1.57   0.117    -.0245963    .0027277
   kr_inz_rate10 |  -.0038508   .0032708    -1.18   0.239    -.0102641    .0025625
   kr_tod_rate10 |   .0156434   .0154066     1.02   0.310    -.0145657    .0458525
           _cons |   -.945482   .1573218    -6.01   0.000    -1.253957   -.6370065
----------------------------------------------------------------------------------

.    
. estimates store November

. ereturn list

scalars:
               e(rank) =  21
               e(ll_0) =  -4090.696684196386
                 e(ll) =  -3889.973873304526
               e(r2_a) =  .1240589565709331
                e(rss) =  2506.594849206837
                e(mss) =  374.3626656307638
               e(rmse) =  .9355856325114956
                 e(r2) =  .1299438341949539
                  e(F) =  21.38432014313926
               e(df_r) =  2863
               e(df_m) =  20
                  e(N) =  2884

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtpoliti c.doubttechnocr c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educy.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "ols"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "iweight"

matrices:
                  e(b) :  1 x 28
                  e(V) :  28 x 28

functions:
             e(sample)   

. 
. suest May November

Simultaneous results for May, November

                                                Number of obs     =      5,852

----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
May_mean         |
         idtecon |  -.0401322   .0236119    -1.70   0.089    -.0864106    .0061461
       1.incloss |   .0406697   .0617389     0.66   0.510    -.0803364    .1616758
       idtfamily |  -.0159061   .0236431    -0.67   0.501    -.0622456    .0304335
1.childrenathome |  -.0113424   .0510682    -0.22   0.824    -.1114343    .0887494
   1.riskgroup01 |  -.3519499   .0430769    -8.17   0.000     -.436379   -.2675207
           1.old |  -.0725836   .0607567    -1.19   0.232    -.1916644    .0464973
         sttecon |    .048747   .0306106     1.59   0.111    -.0112487    .1087427
       sttfamily |    .085693    .027631     3.10   0.002     .0315373    .1398487
     doubtpoliti |   .0900627   .0199528     4.51   0.000      .050956    .1291695
   doubttechnocr |   .0961407   .0217701     4.42   0.000      .053472    .1388093
     protransfer |  -.0132472   .0081623    -1.62   0.105    -.0292451    .0027508
                 |
    leftrightcat |
        1. left  |  -.1262277   .0541975    -2.33   0.020    -.2324529   -.0200026
       3. right  |  -.0136091    .069683    -0.20   0.845    -.1501853    .1229671
   4. DK and NR  |  -.0551539   .0531085    -1.04   0.299    -.1592446    .0489367
                 |
          1.east |   .3042235   .0435705     6.98   0.000     .2188269    .3896201
        gentrust |   .0384124    .009292     4.13   0.000     .0202005    .0566243
        1.female |  -.0793338   .0407793    -1.95   0.052    -.1592598    .0005922
          educyr |   .0016014   .0078435     0.20   0.838    -.0137716    .0169744
   kr_inz_rate10 |  -.0105886   .0365048    -0.29   0.772    -.0821366    .0609595
   kr_tod_rate10 |    .012255   .0214541     0.57   0.568    -.0297944    .0543043
           _cons |  -.9673279   .1846238    -5.24   0.000    -1.329184   -.6054719
-----------------+----------------------------------------------------------------
May_lnvar        |
           _cons |  -.0959385   .0262423    -3.66   0.000    -.1473725   -.0445045
-----------------+----------------------------------------------------------------
November_mean    |
         idtecon |  -.0193202   .0224671    -0.86   0.390    -.0633548    .0247145
       1.incloss |  -.0942348   .0619684    -1.52   0.128    -.2156907    .0272211
       idtfamily |  -.0198307    .022472    -0.88   0.378    -.0638751    .0242137
1.childrenathome |   .0529115   .0525475     1.01   0.314    -.0500797    .1559027
   1.riskgroup01 |  -.1972836   .0418161    -4.72   0.000    -.2792417   -.1153256
           1.old |   .0090457    .057786     0.16   0.876    -.1042127    .1223042
         sttecon |   .0602591   .0304319     1.98   0.048     .0006138    .1199044
       sttfamily |   .0736515   .0285569     2.58   0.010     .0176809     .129622
     doubtpoliti |   .1630565   .0217636     7.49   0.000     .1204007    .2057124
   doubttechnocr |   .0271831    .025113     1.08   0.279    -.0220375    .0764036
     protransfer |  -.0247438   .0074973    -3.30   0.001    -.0394382   -.0100493
                 |
    leftrightcat |
        1. left  |   -.056604   .0522815    -1.08   0.279    -.1590737    .0458658
       3. right  |   .0320212   .0636503     0.50   0.615     -.092731    .1567735
   4. DK and NR  |  -.0133225    .051815    -0.26   0.797     -.114878     .088233
                 |
          1.east |   .2465356   .0455381     5.41   0.000     .1572826    .3357887
        gentrust |   .0474624   .0082225     5.77   0.000     .0313465    .0635782
        1.female |  -.0945254   .0403905    -2.34   0.019    -.1736893   -.0153615
          educyr |  -.0109343   .0078146    -1.40   0.162    -.0262507    .0043821
   kr_inz_rate10 |  -.0038508   .0035795    -1.08   0.282    -.0108666     .003165
   kr_tod_rate10 |   .0156434   .0179859     0.87   0.384    -.0196083    .0508951
           _cons |   -.945482   .1759318    -5.37   0.000    -1.290302   -.6006619
-----------------+----------------------------------------------------------------
November_lnvar   |
           _cons |  -.1331652   .0250343    -5.32   0.000    -.1822315   -.0840989
----------------------------------------------------------------------------------

. test [May_mean]doubtpoliti = [November_mean]doubtpoliti

 ( 1)  [May_mean]doubtpoliti - [November_mean]doubtpoliti = 0

           chi2(  1) =    6.11
         Prob > chi2 =    0.0134

. test [May_mean = November_mean]: c.doubtpoliti

 ( 1)  [May_mean]doubtpoliti - [November_mean]doubtpoliti = 0

           chi2(  1) =    6.11
         Prob > chi2 =    0.0134

. lincom [May_mean]doubtpoliti - [November_mean]doubtpoliti

 ( 1)  [May_mean]doubtpoliti - [November_mean]doubtpoliti = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0729938   .0295257    -2.47   0.013    -.1308632   -.0151244
------------------------------------------------------------------------------

. test [May_mean]c.doubttechnocr=[November_mean]c.doubttechnocr

 ( 1)  [May_mean]doubttechnocr - [November_mean]doubttechnocr = 0

           chi2(  1) =    4.30
         Prob > chi2 =    0.0380

. lincom [May_mean]doubttechnocr - [November_mean]doubttechnocr

 ( 1)  [May_mean]doubttechnocr - [November_mean]doubttechnocr = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0689576   .0332355     2.07   0.038     .0038172    .1340981
------------------------------------------------------------------------------

. 
. 
. *Reviewer comment: Share of at-risk group by age
. tab riskgroup01 old, col chi2

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

riskgroup0 |          old
         1 |         0          1 |     Total
-----------+----------------------+----------
         0 |     3,520        262 |     3,782 
           |     65.09      32.71 |     60.91 
-----------+----------------------+----------
         1 |     1,888        539 |     2,427 
           |     34.91      67.29 |     39.09 
-----------+----------------------+----------
     Total |     5,408        801 |     6,209 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) = 307.2159   Pr = 0.000

. tab riskgroup01 old [iweight=persweight], col 

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

riskgroup0 |          old
         1 |         0          1 |     Total
-----------+----------------------+----------
         0 | 3,450.087    315.701 | 3,765.788 
           |     66.45      32.01 |     60.95 
-----------+----------------------+----------
         1 | 1,741.942    670.683 | 2,412.625 
           |     33.55      67.99 |     39.05 
-----------+----------------------+----------
     Total | 5,192.029 986.383999 | 6,178.413 
           |    100.00     100.00 |    100.00 

. 
. bysort time: tab riskgroup01 old, col chi2

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

riskgroup0 |          old
         1 |         0          1 |     Total
-----------+----------------------+----------
         0 |     1,793        139 |     1,932 
           |     65.94      34.32 |     61.84 
-----------+----------------------+----------
         1 |       926        266 |     1,192 
           |     34.06      65.68 |     38.16 
-----------+----------------------+----------
     Total |     2,719        405 |     3,124 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) = 149.3760   Pr = 0.000

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

riskgroup0 |          old
         1 |         0          1 |     Total
-----------+----------------------+----------
         0 |     1,727        123 |     1,850 
           |     64.22      31.06 |     59.97 
-----------+----------------------+----------
         1 |       962        273 |     1,235 
           |     35.78      68.94 |     40.03 
-----------+----------------------+----------
     Total |     2,689        396 |     3,085 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) = 158.1381   Pr = 0.000


. bysort time: tab riskgroup01 old [iweight=persweight], col 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

riskgroup0 |          old
         1 |         0          1 |     Total
-----------+----------------------+----------
         0 | 1,756.652    169.911 | 1,926.563 
           |     67.31      33.70 |     61.87 
-----------+----------------------+----------
         1 |   853.039    334.218 | 1,187.257 
           |     32.69      66.30 |     38.13 
-----------+----------------------+----------
     Total | 2,609.691    504.129 |  3,113.82 
           |    100.00     100.00 |    100.00 

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

riskgroup0 |          old
         1 |         0          1 |     Total
-----------+----------------------+----------
         0 | 1,693.435     145.79 | 1,839.225 
           |     65.58      30.23 |     60.02 
-----------+----------------------+----------
         1 |   888.903    336.465 | 1,225.368 
           |     34.42      69.77 |     39.98 
-----------+----------------------+----------
     Total | 2,582.338    482.255 | 3,064.593 
           |    100.00     100.00 |    100.00 


. 
.         
. *Reviewer comment: Correlation matrix: multicollinearity?
. pwcorr zopenindex idtecon incloss idtfamily childrenathome  ///
>    riskgroup01 old ///
>    sttecon sttfamily ///
>    doubtstate protransfer leftrightcat ///
>    east  ///   
>    gentrust ///
>    female educyr ///
>    kr_inz_rate10 kr_tod_rate10 [aweight=persweight], sig

             | zopeni~x  idtecon  incloss idtfam~y childr~e riskg~01      old
-------------+---------------------------------------------------------------
  zopenindex |   1.0000 
             |
             |
     idtecon |   0.0590   1.0000 
             |   0.0000
             |
     incloss |   0.0246   0.3571   1.0000 
             |   0.0480   0.0000
             |
   idtfamily |   0.0498   0.5623   0.1295   1.0000 
             |   0.0001   0.0000   0.0000
             |
childrenat~e |   0.0517   0.0899   0.0551   0.0929   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
 riskgroup01 |  -0.1412  -0.0395  -0.0842   0.0160  -0.1723   1.0000 
             |   0.0000   0.0018   0.0000   0.2097   0.0000
             |
         old |  -0.0457  -0.2086  -0.1762  -0.1073  -0.1989   0.2586   1.0000 
             |   0.0002   0.0000   0.0000   0.0000   0.0000   0.0000
             |
     sttecon |   0.1327   0.3485   0.1344   0.2412   0.0511  -0.0186  -0.0261 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.1422   0.0360
             |
   sttfamily |   0.1346   0.2952   0.0496   0.3338   0.1169   0.0048  -0.0284 
             |   0.0000   0.0000   0.0001   0.0000   0.0000   0.7048   0.0227
             |
  doubtstate |   0.2564   0.2950   0.0912   0.2806   0.0960  -0.0371  -0.1127 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0035   0.0000
             |
 protransfer |  -0.0327   0.1453   0.0330   0.1361   0.0233   0.0119  -0.0404 
             |   0.0088   0.0000   0.0082   0.0000   0.0639   0.3504   0.0012
             |
leftrightcat |   0.0453   0.0932   0.0251   0.0696   0.0037   0.0200  -0.0056 
             |   0.0003   0.0000   0.0439   0.0000   0.7651   0.1151   0.6550
             |
        east |   0.1141  -0.0010  -0.0103   0.0287  -0.0149   0.0216   0.0279 
             |   0.0000   0.9370   0.4082   0.0217   0.2346   0.0885   0.0248
             |
    gentrust |   0.0126  -0.1361  -0.0554  -0.1062  -0.0645  -0.0222   0.0881 
             |   0.3113   0.0000   0.0000   0.0000   0.0000   0.0813   0.0000
             |
      female |  -0.0350  -0.0233  -0.0538   0.0031   0.0382   0.0488   0.0453 
             |   0.0049   0.0616   0.0000   0.8039   0.0023   0.0001   0.0003
             |
      educyr |  -0.0184  -0.1320  -0.0067  -0.1078   0.0427  -0.1461  -0.0960 
             |   0.1406   0.0000   0.5902   0.0000   0.0007   0.0000   0.0000
             |
kr_inz_ra~10 |  -0.0167   0.0696  -0.0086   0.0851  -0.0061   0.0046  -0.0121 
             |   0.1818   0.0000   0.4946   0.0000   0.6266   0.7172   0.3336
             |
kr_tod_ra~10 |   0.0078   0.0361   0.0472   0.0299   0.0290  -0.0234  -0.0230 
             |   0.5344   0.0040   0.0002   0.0174   0.0216   0.0678   0.0664
             |

             |  sttecon sttfam~y doubts~e protra~r leftr~at     east gentrust
-------------+---------------------------------------------------------------
     sttecon |   1.0000 
             |
             |
   sttfamily |   0.5819   1.0000 
             |   0.0000
             |
  doubtstate |   0.2527   0.2383   1.0000 
             |   0.0000   0.0000
             |
 protransfer |   0.0040   0.0662   0.0756   1.0000 
             |   0.7515   0.0000   0.0000
             |
leftrightcat |   0.1299   0.0873   0.1384  -0.0934   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
        east |   0.0081   0.0204   0.0574   0.0526  -0.0316   1.0000 
             |   0.5168   0.1024   0.0000   0.0000   0.0112
             |
    gentrust |  -0.1539  -0.1311  -0.3002   0.0255  -0.1059   0.0062   1.0000 
             |   0.0000   0.0000   0.0000   0.0413   0.0000   0.6200
             |
      female |   0.1013   0.1194  -0.0012  -0.0063   0.0932   0.0009  -0.0234 
             |   0.0000   0.0000   0.9230   0.6136   0.0000   0.9431   0.0600
             |
      educyr |  -0.0620  -0.0788  -0.2118  -0.0910  -0.1487   0.0415   0.1709 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0009   0.0000
             |
kr_inz_ra~10 |   0.0184   0.0165  -0.0089  -0.0163   0.0169  -0.1366   0.0172 
             |   0.1418   0.1885   0.4761   0.1946   0.1778   0.0000   0.1708
             |
kr_tod_ra~10 |   0.0332   0.0207   0.0256   0.0151   0.0180  -0.2078   0.0154 
             |   0.0081   0.1005   0.0411   0.2298   0.1517   0.0000   0.2215
             |

             |   female   educyr kr_in~10 kr_t~e10
-------------+------------------------------------
      female |   1.0000 
             |
             |
      educyr |  -0.0763   1.0000 
             |   0.0000
             |
kr_inz_ra~10 |   0.0024   0.0044   1.0000 
             |   0.8501   0.7286
             |
kr_tod_ra~10 |  -0.0310   0.0101   0.3108   1.0000 
             |   0.0133   0.4238   0.0000
             |

.    
. *...self-interest indicators only
. bysort time: pwcorr idtecon incloss idtfamily childrenathome riskgroup01 old ///
>    [aweight=persweight], sig

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

             |  idtecon  incloss idtfam~y childr~e riskg~01      old
-------------+------------------------------------------------------
     idtecon |   1.0000 
             |
             |
     incloss |   0.3529   1.0000 
             |   0.0000
             |
   idtfamily |   0.5315   0.1263   1.0000 
             |   0.0000   0.0000
             |
childrenat~e |   0.1045   0.0477   0.1185   1.0000 
             |   0.0000   0.0069   0.0000
             |
 riskgroup01 |  -0.0301  -0.0762   0.0267  -0.1759   1.0000 
             |   0.0933   0.0000   0.1368   0.0000
             |
         old |  -0.2134  -0.1806  -0.1151  -0.2058   0.2549   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

             |  idtecon  incloss idtfam~y childr~e riskg~01      old
-------------+------------------------------------------------------
     idtecon |   1.0000 
             |
             |
     incloss |   0.3699   1.0000 
             |   0.0000
             |
   idtfamily |   0.5835   0.1414   1.0000 
             |   0.0000   0.0000
             |
childrenat~e |   0.0781   0.0628   0.0721   1.0000 
             |   0.0000   0.0004   0.0001
             |
 riskgroup01 |  -0.0519  -0.0916   0.0023  -0.1681   1.0000 
             |   0.0039   0.0000   0.9001   0.0000
             |
         old |  -0.2052  -0.1720  -0.1013  -0.1918   0.2628   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |

. 
. *...check VIF
. reg zopenindex c.idtecon c.idtfamily  [pweight=persweight] if time==0
(sum of wgt is 3,231.65399813652)

Linear regression                               Number of obs     =      3,232
                                                F(2, 3229)        =       2.05
                                                Prob > F          =     0.1290
                                                R-squared         =     0.0020
                                                Root MSE          =     1.0013

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     idtecon |   .0309446   .0210715     1.47   0.142    -.0103703    .0722595
   idtfamily |   .0122278   .0227576     0.54   0.591     -.032393    .0568486
       _cons |  -.0992989   .0519442    -1.91   0.056    -.2011459    .0025481
------------------------------------------------------------------------------

. 
. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     idtecon |      1.39    0.717472
   idtfamily |      1.39    0.717472
-------------+----------------------
    Mean VIF |      1.39

. 
. reg zopenindex c.idtecon c.idtfamily  [pweight=persweight] if time==1
(sum of wgt is 3,175.82700002193)

Linear regression                               Number of obs     =      3,180
                                                F(2, 3177)        =       6.32
                                                Prob > F          =     0.0018
                                                R-squared         =     0.0061
                                                Root MSE          =     .99829

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     idtecon |   .0403111   .0202266     1.99   0.046     .0006527    .0799696
   idtfamily |   .0324971   .0216294     1.50   0.133    -.0099119    .0749061
       _cons |  -.1767338   .0510491    -3.46   0.001    -.2768264   -.0766412
------------------------------------------------------------------------------

. 
. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     idtecon |      1.52    0.659494
   idtfamily |      1.52    0.659494
-------------+----------------------
    Mean VIF |      1.52

.         
.         
. * Estimate VIF to check for multicollinearity in final models
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==0
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(19, 2915)       =      18.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1151
                                                Root MSE          =     .95313

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0387569   .0236479    -1.64   0.101    -.0851252    .0076114
       1.incloss |   .0411791   .0620902     0.66   0.507     -.080566    .1629241
       idtfamily |  -.0151395   .0237214    -0.64   0.523     -.061652     .031373
1.childrenathome |  -.0126736   .0512708    -0.25   0.805    -.1132042     .087857
   1.riskgroup01 |  -.3525135   .0431559    -8.17   0.000    -.4371326   -.2678944
           1.old |  -.0691034   .0607612    -1.14   0.256    -.1882427    .0500359
         sttecon |   .0477287   .0306149     1.56   0.119    -.0123003    .1077577
       sttfamily |   .0850086    .027782     3.06   0.002     .0305343     .139483
      doubtstate |   .1830349   .0186037     9.84   0.000     .1465572    .2195125
     protransfer |    -.01333   .0081913    -1.63   0.104    -.0293914    .0027314
                 |
    leftrightcat |
        1. left  |  -.1259192    .054463    -2.31   0.021    -.2327092   -.0191293
       3. right  |  -.0182548   .0697313    -0.26   0.794    -.1549824    .1184729
   4. DK and NR  |  -.0548026    .053331    -1.03   0.304    -.1593729    .0497677
                 |
          1.east |   .3038457   .0436968     6.95   0.000     .2181659    .3895255
        gentrust |   .0386436   .0093263     4.14   0.000     .0203568    .0569304
        1.female |  -.0797826    .040936    -1.95   0.051     -.160049    .0004838
          educyr |   .0014154   .0078617     0.18   0.857    -.0139996    .0168303
   kr_inz_rate10 |  -.0095933   .0365551    -0.26   0.793    -.0812698    .0620832
   kr_tod_rate10 |   .0125349   .0214855     0.58   0.560    -.0295934    .0546631
           _cons |  -.9662276   .1852152    -5.22   0.000    -1.329394   -.6030618
----------------------------------------------------------------------------------

. 
. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     idtecon |      1.75    0.570710
   1.incloss |      1.17    0.852192
   idtfamily |      1.51    0.663874
1.children~e |      1.09    0.914416
1.riskgro~01 |      1.12    0.894866
       1.old |      1.18    0.846118
     sttecon |      1.54    0.648439
   sttfamily |      1.53    0.651695
  doubtstate |      1.27    0.786544
 protransfer |      1.10    0.907220
leftrightcat |
          1  |      1.18    0.846678
          3  |      1.14    0.877095
          4  |      1.21    0.826820
      1.east |      1.09    0.920042
    gentrust |      1.17    0.856941
    1.female |      1.08    0.926592
      educyr |      1.14    0.875571
kr_inz_ra~10 |      1.10    0.909201
kr_tod_ra~10 |      1.11    0.901278
-------------+----------------------
    Mean VIF |      1.24

. 
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==1
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(19, 2899)       =      17.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1267
                                                Root MSE          =     .93681

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0215578   .0225017    -0.96   0.338    -.0656788    .0225632
       1.incloss |  -.0992317    .062115    -1.60   0.110    -.2210258    .0225623
       idtfamily |  -.0246457   .0224904    -1.10   0.273    -.0687444    .0194531
1.childrenathome |   .0544339   .0528406     1.03   0.303    -.0491749    .1580428
   1.riskgroup01 |  -.1933677   .0419116    -4.61   0.000    -.2755472   -.1111882
           1.old |     .01391    .057794     0.24   0.810    -.0994116    .1272315
         sttecon |    .072711   .0306416     2.37   0.018     .0126294    .1327925
       sttfamily |   .0723234   .0285233     2.54   0.011     .0163954    .1282513
      doubtstate |   .1989073   .0188034    10.58   0.000      .162038    .2357766
     protransfer |  -.0253399   .0075696    -3.35   0.001    -.0401822   -.0104976
                 |
    leftrightcat |
        1. left  |  -.0547614   .0525002    -1.04   0.297    -.1577029    .0481801
       3. right  |   .0421067   .0641434     0.66   0.512    -.0836645    .1678779
   4. DK and NR  |   -.012234   .0521515    -0.23   0.815    -.1144917    .0900237
                 |
          1.east |   .2531371   .0455845     5.55   0.000     .1637558    .3425183
        gentrust |   .0464572   .0082817     5.61   0.000     .0302186    .0626957
        1.female |   -.099572   .0406831    -2.45   0.014    -.1793428   -.0198012
          educyr |  -.0109418   .0078301    -1.40   0.162     -.026295    .0044114
   kr_inz_rate10 |  -.0037708   .0035924    -1.05   0.294    -.0108147     .003273
   kr_tod_rate10 |   .0155799   .0180671     0.86   0.389    -.0198457    .0510056
           _cons |  -.9491665   .1762996    -5.38   0.000    -1.294852   -.6034813
----------------------------------------------------------------------------------

. 
. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     idtecon |      1.98    0.504551
   1.incloss |      1.21    0.826318
   idtfamily |      1.66    0.603163
1.children~e |      1.09    0.919563
1.riskgro~01 |      1.12    0.890727
       1.old |      1.17    0.851819
     sttecon |      1.84    0.542041
   sttfamily |      1.82    0.549846
  doubtstate |      1.38    0.725863
 protransfer |      1.07    0.932056
leftrightcat |
          1  |      1.18    0.849236
          3  |      1.15    0.868404
          4  |      1.21    0.826879
      1.east |      1.18    0.849328
    gentrust |      1.14    0.878147
    1.female |      1.06    0.940488
      educyr |      1.16    0.863992
kr_inz_ra~10 |      1.21    0.825314
kr_tod_ra~10 |      1.13    0.888752
-------------+----------------------
    Mean VIF |      1.30

.         
. *Check whether effects change if introduced separately into the models
. *Re-run main model for May
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==0
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(19, 2915)       =      18.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1151
                                                Root MSE          =     .95313

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0387569   .0236479    -1.64   0.101    -.0851252    .0076114
       1.incloss |   .0411791   .0620902     0.66   0.507     -.080566    .1629241
       idtfamily |  -.0151395   .0237214    -0.64   0.523     -.061652     .031373
1.childrenathome |  -.0126736   .0512708    -0.25   0.805    -.1132042     .087857
   1.riskgroup01 |  -.3525135   .0431559    -8.17   0.000    -.4371326   -.2678944
           1.old |  -.0691034   .0607612    -1.14   0.256    -.1882427    .0500359
         sttecon |   .0477287   .0306149     1.56   0.119    -.0123003    .1077577
       sttfamily |   .0850086    .027782     3.06   0.002     .0305343     .139483
      doubtstate |   .1830349   .0186037     9.84   0.000     .1465572    .2195125
     protransfer |    -.01333   .0081913    -1.63   0.104    -.0293914    .0027314
                 |
    leftrightcat |
        1. left  |  -.1259192    .054463    -2.31   0.021    -.2327092   -.0191293
       3. right  |  -.0182548   .0697313    -0.26   0.794    -.1549824    .1184729
   4. DK and NR  |  -.0548026    .053331    -1.03   0.304    -.1593729    .0497677
                 |
          1.east |   .3038457   .0436968     6.95   0.000     .2181659    .3895255
        gentrust |   .0386436   .0093263     4.14   0.000     .0203568    .0569304
        1.female |  -.0797826    .040936    -1.95   0.051     -.160049    .0004838
          educyr |   .0014154   .0078617     0.18   0.857    -.0139996    .0168303
   kr_inz_rate10 |  -.0095933   .0365551    -0.26   0.793    -.0812698    .0620832
   kr_tod_rate10 |   .0125349   .0214855     0.58   0.560    -.0295934    .0546631
           _cons |  -.9662276   .1852152    -5.22   0.000    -1.329394   -.6030618
----------------------------------------------------------------------------------

. 
. gen modelsample1 = e(sample)

. 
. *Run bivariate regressions for May
. reg zopenindex c.idtecon [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       2.68
                                                Prob > F          =     0.1017
                                                R-squared         =     0.0014
                                                Root MSE          =     1.0094

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     idtecon |   .0323657   .0197718     1.64   0.102    -.0064023    .0711338
       _cons |  -.0726082   .0494424    -1.47   0.142    -.1695536    .0243371
------------------------------------------------------------------------------

. reg zopenindex i.incloss [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       3.50
                                                Prob > F          =     0.0615
                                                R-squared         =     0.0017
                                                Root MSE          =     1.0092

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.incloss |   .1148265   .0613906     1.87   0.062    -.0055467    .2351996
       _cons |  -.0153797   .0225443    -0.68   0.495    -.0595841    .0288246
------------------------------------------------------------------------------

. reg zopenindex c.idtfamily [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       2.78
                                                Prob > F          =     0.0953
                                                R-squared         =     0.0015
                                                Root MSE          =     1.0094

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   idtfamily |   .0356418   .0213624     1.67   0.095    -.0062449    .0775286
       _cons |  -.0701508   .0475909    -1.47   0.141    -.1634658    .0231643
------------------------------------------------------------------------------

. reg zopenindex i.childrenathome [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       6.22
                                                Prob > F          =     0.0127
                                                R-squared         =     0.0025
                                                Root MSE          =     1.0089

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
1.childrenathome |   .1282003   .0513878     2.49   0.013     .0274405    .2289601
           _cons |  -.0217263   .0236341    -0.92   0.358    -.0680675    .0246148
----------------------------------------------------------------------------------

. reg zopenindex i.riskgroup01 [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =      78.79
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0321
                                                Root MSE          =      .9938

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.riskgroup01 |  -.3723795   .0419522    -8.88   0.000    -.4546383   -.2901207
        _cons |   .1450455   .0270563     5.36   0.000     .0919942    .1980968
-------------------------------------------------------------------------------

. reg zopenindex i.old [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =      13.39
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0060
                                                Root MSE          =     1.0071

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       1.old |  -.2154264   .0588621    -3.66   0.000    -.3308416   -.1000112
       _cons |   .0369206     .02273     1.62   0.104    -.0076478     .081489
------------------------------------------------------------------------------

. reg zopenindex c.sttecon [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =      14.41
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0078
                                                Root MSE          =     1.0062

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     sttecon |   .1036096   .0272923     3.80   0.000     .0500955    .1571237
       _cons |  -.3922285   .1042425    -3.76   0.000    -.5966244   -.1878326
------------------------------------------------------------------------------

. reg zopenindex c.sttfamily [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =      21.17
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0110
                                                Root MSE          =     1.0046

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   sttfamily |   .1115131   .0242384     4.60   0.000     .0639871     .159039
       _cons |  -.3931781   .0861137    -4.57   0.000    -.5620275   -.2243287
------------------------------------------------------------------------------

. reg zopenindex c.doubtstate [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =     103.51
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0503
                                                Root MSE          =     .98441

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  doubtstate |   .1706717   .0167755    10.17   0.000     .1377788    .2035646
       _cons |  -.5994529   .0603046    -9.94   0.000    -.7176965   -.4812092
------------------------------------------------------------------------------

. reg zopenindex c.protransfer [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       0.68
                                                Prob > F          =     0.4091
                                                R-squared         =     0.0003
                                                Root MSE          =       1.01

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 protransfer |  -.0068018   .0082381    -0.83   0.409    -.0229548    .0093512
       _cons |   .0310674   .0399479     0.78   0.437    -.0472613    .1093961
------------------------------------------------------------------------------

. reg zopenindex ib2.leftrightcat [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(3, 2931)        =       4.38
                                                Prob > F          =     0.0044
                                                R-squared         =     0.0054
                                                Root MSE          =     1.0077

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
 leftrightcat |
     1. left  |  -.1704738   .0537009    -3.17   0.002    -.2757692   -.0651785
    3. right  |   .0733699    .074803     0.98   0.327    -.0733019    .2200416
4. DK and NR  |  -.0484539   .0545956    -0.89   0.375    -.1555035    .0585957
              |
        _cons |   .0369366   .0300421     1.23   0.219    -.0219691    .0958423
-------------------------------------------------------------------------------

. reg zopenindex i.east [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =      56.15
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0136
                                                Root MSE          =     1.0032

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      1.east |    .327398    .043692     7.49   0.000      .241728     .413068
       _cons |  -.0470287    .023849    -1.97   0.049    -.0937911   -.0002663
------------------------------------------------------------------------------

. reg zopenindex c.gentrust [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       0.08
                                                Prob > F          =     0.7731
                                                R-squared         =     0.0000
                                                Root MSE          =     1.0101

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    gentrust |   .0026033   .0090285     0.29   0.773    -.0150996    .0203062
       _cons |  -.0077531   .0456429    -0.17   0.865    -.0972485    .0817424
------------------------------------------------------------------------------

. reg zopenindex i.female [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       4.39
                                                Prob > F          =     0.0362
                                                R-squared         =     0.0019
                                                Root MSE          =     1.0092

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    1.female |  -.0881216    .042061    -2.10   0.036    -.1705937   -.0056495
       _cons |   .0478172   .0300635     1.59   0.112    -.0111304    .1067648
------------------------------------------------------------------------------

. reg zopenindex c.educyr [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       0.19
                                                Prob > F          =     0.6608
                                                R-squared         =     0.0001
                                                Root MSE          =     1.0101

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      educyr |   .0033991   .0077442     0.44   0.661    -.0117855    .0185837
       _cons |  -.0433771   .1102597    -0.39   0.694    -.2595713    .1728171
------------------------------------------------------------------------------

. reg zopenindex c.kr_inz_rate10 [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       0.70
                                                Prob > F          =     0.4030
                                                R-squared         =     0.0003
                                                Root MSE          =       1.01

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
kr_inz_rate10 |  -.0283391   .0338858    -0.84   0.403    -.0947815    .0381034
        _cons |   .0171171   .0265463     0.64   0.519    -.0349342    .0691684
-------------------------------------------------------------------------------

. reg zopenindex c.kr_tod_rate10 [pweight=persweight] if(time==0 & modelsample1==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(1, 2933)        =       0.01
                                                Prob > F          =     0.9224
                                                R-squared         =     0.0000
                                                Root MSE          =     1.0101

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
kr_tod_rate10 |  -.0021789   .0223765    -0.10   0.922    -.0460542    .0416963
        _cons |   .0053531   .0297237     0.18   0.857    -.0529283    .0636345
-------------------------------------------------------------------------------

. 
. *Re-run main model for November
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==1
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(19, 2899)       =      17.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1267
                                                Root MSE          =     .93681

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0215578   .0225017    -0.96   0.338    -.0656788    .0225632
       1.incloss |  -.0992317    .062115    -1.60   0.110    -.2210258    .0225623
       idtfamily |  -.0246457   .0224904    -1.10   0.273    -.0687444    .0194531
1.childrenathome |   .0544339   .0528406     1.03   0.303    -.0491749    .1580428
   1.riskgroup01 |  -.1933677   .0419116    -4.61   0.000    -.2755472   -.1111882
           1.old |     .01391    .057794     0.24   0.810    -.0994116    .1272315
         sttecon |    .072711   .0306416     2.37   0.018     .0126294    .1327925
       sttfamily |   .0723234   .0285233     2.54   0.011     .0163954    .1282513
      doubtstate |   .1989073   .0188034    10.58   0.000      .162038    .2357766
     protransfer |  -.0253399   .0075696    -3.35   0.001    -.0401822   -.0104976
                 |
    leftrightcat |
        1. left  |  -.0547614   .0525002    -1.04   0.297    -.1577029    .0481801
       3. right  |   .0421067   .0641434     0.66   0.512    -.0836645    .1678779
   4. DK and NR  |   -.012234   .0521515    -0.23   0.815    -.1144917    .0900237
                 |
          1.east |   .2531371   .0455845     5.55   0.000     .1637558    .3425183
        gentrust |   .0464572   .0082817     5.61   0.000     .0302186    .0626957
        1.female |   -.099572   .0406831    -2.45   0.014    -.1793428   -.0198012
          educyr |  -.0109418   .0078301    -1.40   0.162     -.026295    .0044114
   kr_inz_rate10 |  -.0037708   .0035924    -1.05   0.294    -.0108147     .003273
   kr_tod_rate10 |   .0155799   .0180671     0.86   0.389    -.0198457    .0510056
           _cons |  -.9491665   .1762996    -5.38   0.000    -1.294852   -.6034813
----------------------------------------------------------------------------------

.    
. gen modelsample2 = e(sample)

. 
. *Run bivariate regressions for November
. reg zopenindex c.idtecon [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       8.26
                                                Prob > F          =     0.0041
                                                R-squared         =     0.0041
                                                Root MSE          =     .99729

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     idtecon |   .0526448   .0183144     2.87   0.004     .0167342    .0885554
       _cons |  -.1387076   .0477515    -2.90   0.004    -.2323378   -.0450775
------------------------------------------------------------------------------

. reg zopenindex i.incloss [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       0.03
                                                Prob > F          =     0.8582
                                                R-squared         =     0.0000
                                                Root MSE          =     .99936

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.incloss |  -.0113399   .0634867    -0.18   0.858    -.1358232    .1131434
       _cons |  -.0041504    .022002    -0.19   0.850    -.0472913    .0389906
------------------------------------------------------------------------------

. reg zopenindex c.idtfamily [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       6.88
                                                Prob > F          =     0.0088
                                                R-squared         =     0.0036
                                                Root MSE          =     .99756

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   idtfamily |   .0517161   .0197135     2.62   0.009     .0130623    .0903699
       _cons |  -.1243639   .0474263    -2.62   0.009    -.2173564   -.0313714
------------------------------------------------------------------------------

. reg zopenindex i.childrenathome [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       7.48
                                                Prob > F          =     0.0063
                                                R-squared         =     0.0034
                                                Root MSE          =     .99766

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
1.childrenathome |   .1503188   .0549441     2.74   0.006     .0425857     .258052
           _cons |  -.0335314   .0226435    -1.48   0.139    -.0779302    .0108675
----------------------------------------------------------------------------------

. reg zopenindex i.riskgroup01 [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =      33.73
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0136
                                                Root MSE          =     .99255

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.riskgroup01 |  -.2378166   .0409467    -5.81   0.000     -.318104   -.1575293
        _cons |   .0893566   .0278019     3.21   0.001     .0348433    .1438699
-------------------------------------------------------------------------------

. reg zopenindex i.old [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       1.58
                                                Prob > F          =     0.2094
                                                R-squared         =     0.0006
                                                Root MSE          =     .99906

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       1.old |  -.0684844   .0545489    -1.26   0.209    -.1754426    .0384738
       _cons |   .0049115   .0227004     0.22   0.829    -.0395989     .049422
------------------------------------------------------------------------------

. reg zopenindex c.sttecon [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =      62.01
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0272
                                                Root MSE          =     .98567

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     sttecon |   .1914306   .0243096     7.87   0.000     .1437649    .2390963
       _cons |  -.7402662   .0919759    -8.05   0.000    -.9206104   -.5599219
------------------------------------------------------------------------------

. reg zopenindex c.sttfamily [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =      54.84
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0246
                                                Root MSE          =     .98698

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   sttfamily |   .1664751   .0224812     7.41   0.000     .1223944    .2105557
       _cons |  -.6018673    .079628    -7.56   0.000    -.7580001   -.4457345
------------------------------------------------------------------------------

. reg zopenindex c.doubtstate [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =     148.24
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0754
                                                Root MSE          =     .96096

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  doubtstate |   .2028739   .0166629    12.18   0.000     .1702017    .2355461
       _cons |  -.7142797   .0566765   -12.60   0.000    -.8254097   -.6031497
------------------------------------------------------------------------------

. reg zopenindex c.protransfer [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       4.47
                                                Prob > F          =     0.0346
                                                R-squared         =     0.0020
                                                Root MSE          =     .99834

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 protransfer |  -.0164509    .007784    -2.11   0.035    -.0317135   -.0011883
       _cons |    .059811   .0371085     1.61   0.107    -.0129505    .1325725
------------------------------------------------------------------------------

. reg zopenindex ib2.leftrightcat [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(3, 2915)        =       4.94
                                                Prob > F          =     0.0020
                                                R-squared         =     0.0064
                                                Root MSE          =     .99649

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
 leftrightcat |
     1. left  |  -.1408214   .0544966    -2.58   0.010    -.2476771   -.0339657
    3. right  |   .1417352   .0693824     2.04   0.041     .0056917    .2777787
4. DK and NR  |   .0189698   .0525174     0.36   0.718    -.0840051    .1219448
              |
        _cons |  -.0036667   .0302975    -0.12   0.904    -.0630734    .0557399
-------------------------------------------------------------------------------

. reg zopenindex i.east [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =      48.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0116
                                                Root MSE          =     .99354

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      1.east |   .2969628   .0425004     6.99   0.000      .213629    .3802967
       _cons |  -.0519661   .0234953    -2.21   0.027    -.0980352    -.005897
------------------------------------------------------------------------------

. reg zopenindex c.gentrust [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       1.73
                                                Prob > F          =     0.1885
                                                R-squared         =     0.0008
                                                Root MSE          =     .99895

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    gentrust |   .0111563   .0084819     1.32   0.189    -.0054748    .0277873
       _cons |  -.0531087   .0439342    -1.21   0.227    -.1392538    .0330365
------------------------------------------------------------------------------

. reg zopenindex i.female [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       2.68
                                                Prob > F          =     0.1016
                                                R-squared         =     0.0012
                                                Root MSE          =     .99879

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    1.female |  -.0677931   .0413903    -1.64   0.102    -.1489502    .0133641
       _cons |   .0290773   .0301419     0.96   0.335    -.0300243    .0881789
------------------------------------------------------------------------------

. reg zopenindex c.educyr [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       5.36
                                                Prob > F          =     0.0207
                                                R-squared         =     0.0023
                                                Root MSE          =      .9982

------------------------------------------------------------------------------
             |               Robust
  zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      educyr |  -.0179003   .0077343    -2.31   0.021    -.0330656   -.0027349
       _cons |   .2400437   .1109703     2.16   0.031     .0224556    .4576318
------------------------------------------------------------------------------

. reg zopenindex c.kr_inz_rate10 [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       4.99
                                                Prob > F          =     0.0256
                                                R-squared         =     0.0019
                                                Root MSE          =     .99841

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
kr_inz_rate10 |  -.0074346   .0033284    -2.23   0.026    -.0139608   -.0009085
        _cons |   .0957063   .0490227     1.95   0.051    -.0004163    .1918288
-------------------------------------------------------------------------------

. reg zopenindex c.kr_tod_rate10 [pweight=persweight] if(time==1 & modelsample2==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(1, 2917)        =       0.12
                                                Prob > F          =     0.7328
                                                R-squared         =     0.0001
                                                Root MSE          =     .99934

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
kr_tod_rate10 |    .006173   .0180819     0.34   0.733    -.0292815    .0416276
        _cons |  -.0153336   .0328015    -0.47   0.640    -.0796501    .0489829
-------------------------------------------------------------------------------

. 
. 
. *Editor remark: trust variables are probably not confounders, but mediators
. *Models without the two trust variables
. *Plot all models into one coefplot
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==0
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(19, 2915)       =      18.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1151
                                                Root MSE          =     .95313

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0387569   .0236479    -1.64   0.101    -.0851252    .0076114
       1.incloss |   .0411791   .0620902     0.66   0.507     -.080566    .1629241
       idtfamily |  -.0151395   .0237214    -0.64   0.523     -.061652     .031373
1.childrenathome |  -.0126736   .0512708    -0.25   0.805    -.1132042     .087857
   1.riskgroup01 |  -.3525135   .0431559    -8.17   0.000    -.4371326   -.2678944
           1.old |  -.0691034   .0607612    -1.14   0.256    -.1882427    .0500359
         sttecon |   .0477287   .0306149     1.56   0.119    -.0123003    .1077577
       sttfamily |   .0850086    .027782     3.06   0.002     .0305343     .139483
      doubtstate |   .1830349   .0186037     9.84   0.000     .1465572    .2195125
     protransfer |    -.01333   .0081913    -1.63   0.104    -.0293914    .0027314
                 |
    leftrightcat |
        1. left  |  -.1259192    .054463    -2.31   0.021    -.2327092   -.0191293
       3. right  |  -.0182548   .0697313    -0.26   0.794    -.1549824    .1184729
   4. DK and NR  |  -.0548026    .053331    -1.03   0.304    -.1593729    .0497677
                 |
          1.east |   .3038457   .0436968     6.95   0.000     .2181659    .3895255
        gentrust |   .0386436   .0093263     4.14   0.000     .0203568    .0569304
        1.female |  -.0797826    .040936    -1.95   0.051     -.160049    .0004838
          educyr |   .0014154   .0078617     0.18   0.857    -.0139996    .0168303
   kr_inz_rate10 |  -.0095933   .0365551    -0.26   0.793    -.0812698    .0620832
   kr_tod_rate10 |   .0125349   .0214855     0.58   0.560    -.0295934    .0546631
           _cons |  -.9662276   .1852152    -5.22   0.000    -1.329394   -.6030618
----------------------------------------------------------------------------------

. 
. estimates store May

. ereturn list

scalars:
               e(rank) =  20
               e(ll_0) =  -4193.165390729501
                 e(ll) =  -4013.651454369443
               e(r2_a) =  .109372939662936
                e(rss) =  2648.138476838018
                e(mss) =  344.5833810346576
               e(rmse) =  .9531276449999381
                 e(r2) =  .1151404632302176
                  e(F) =  18.53640081511627
               e(df_r) =  2915
               e(df_m) =  19
                  e(N) =  2935

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtstate c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educyr    c.kr_inz_rat.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "robust"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "pweight"
            e(vcetype) : "Robust"

matrices:
                  e(b) :  1 x 27
                  e(V) :  27 x 27
       e(V_modelbased) :  27 x 27

functions:
             e(sample)   

. 
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==1
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(19, 2899)       =      17.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1267
                                                Root MSE          =     .93681

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0215578   .0225017    -0.96   0.338    -.0656788    .0225632
       1.incloss |  -.0992317    .062115    -1.60   0.110    -.2210258    .0225623
       idtfamily |  -.0246457   .0224904    -1.10   0.273    -.0687444    .0194531
1.childrenathome |   .0544339   .0528406     1.03   0.303    -.0491749    .1580428
   1.riskgroup01 |  -.1933677   .0419116    -4.61   0.000    -.2755472   -.1111882
           1.old |     .01391    .057794     0.24   0.810    -.0994116    .1272315
         sttecon |    .072711   .0306416     2.37   0.018     .0126294    .1327925
       sttfamily |   .0723234   .0285233     2.54   0.011     .0163954    .1282513
      doubtstate |   .1989073   .0188034    10.58   0.000      .162038    .2357766
     protransfer |  -.0253399   .0075696    -3.35   0.001    -.0401822   -.0104976
                 |
    leftrightcat |
        1. left  |  -.0547614   .0525002    -1.04   0.297    -.1577029    .0481801
       3. right  |   .0421067   .0641434     0.66   0.512    -.0836645    .1678779
   4. DK and NR  |   -.012234   .0521515    -0.23   0.815    -.1144917    .0900237
                 |
          1.east |   .2531371   .0455845     5.55   0.000     .1637558    .3425183
        gentrust |   .0464572   .0082817     5.61   0.000     .0302186    .0626957
        1.female |   -.099572   .0406831    -2.45   0.014    -.1793428   -.0198012
          educyr |  -.0109418   .0078301    -1.40   0.162     -.026295    .0044114
   kr_inz_rate10 |  -.0037708   .0035924    -1.05   0.294    -.0108147     .003273
   kr_tod_rate10 |   .0155799   .0180671     0.86   0.389    -.0198457    .0510056
           _cons |  -.9491665   .1762996    -5.38   0.000    -1.294852   -.6034813
----------------------------------------------------------------------------------

.    
. estimates store November

. ereturn list

scalars:
               e(rank) =  20
               e(ll_0) =  -4139.027564002477
                 e(ll) =  -3941.322611588155
               e(r2_a) =  .1209628704102026
                e(rss) =  2544.221924526387
                e(mss) =  369.0756239448938
               e(rmse) =  .9368140331885634
                 e(r2) =  .1266865528852561
                  e(F) =  17.67712594538185
               e(df_r) =  2899
               e(df_m) =  19
                  e(N) =  2919

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtstate c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educyr    c.kr_inz_rat.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "robust"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "pweight"
            e(vcetype) : "Robust"

matrices:
                  e(b) :  1 x 27
                  e(V) :  27 x 27
       e(V_modelbased) :  27 x 27

functions:
             e(sample)   

. 
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==0
(sum of wgt is 2,917.46899853647)

Linear regression                               Number of obs     =      2,942
                                                F(17, 2924)       =      12.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0686
                                                Root MSE          =       .977

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0226518   .0239407    -0.95   0.344    -.0695943    .0242906
       1.incloss |   .0390559   .0639661     0.61   0.542    -.0863671     .164479
       idtfamily |   .0162481   .0241365     0.67   0.501    -.0310782    .0635744
1.childrenathome |   .0075456   .0532326     0.14   0.887    -.0968317    .1119228
   1.riskgroup01 |  -.3633445   .0441644    -8.23   0.000    -.4499411    -.276748
           1.old |  -.0898714    .060463    -1.49   0.137    -.2084258    .0286831
         sttecon |   .0542598   .0313387     1.73   0.083    -.0071883    .1157079
       sttfamily |   .0919794   .0280242     3.28   0.001     .0370303    .1469285
     protransfer |  -.0080454   .0083418    -0.96   0.335    -.0244018     .008311
                 |
    leftrightcat |
        1. left  |  -.1434072    .054746    -2.62   0.009    -.2507518   -.0360627
       3. right  |   .0401225   .0724634     0.55   0.580    -.1019621     .182207
   4. DK and NR  |  -.0411136     .05439    -0.76   0.450    -.1477602    .0655329
                 |
          1.east |   .3540778   .0450851     7.85   0.000     .2656759    .4424796
        1.female |  -.0911737   .0419267    -2.17   0.030    -.1733826   -.0089647
          educyr |  -.0077095   .0079679    -0.97   0.333    -.0233327    .0079137
   kr_inz_rate10 |   .0036011   .0372934     0.10   0.923    -.0695229    .0767252
   kr_tod_rate10 |   .0152156   .0230733     0.66   0.510    -.0300259    .0604571
           _cons |  -.2216718   .1744935    -1.27   0.204    -.5638144    .1204707
----------------------------------------------------------------------------------

. 
. estimates store Maywithout

. ereturn list

scalars:
               e(rank) =  18
               e(ll_0) =  -4201.613628571783
                 e(ll) =  -4097.044641387687
               e(r2_a) =  .0632041430265831
                e(rss) =  2791.064468435128
                e(mss) =  205.6306669462397
               e(rmse) =  .9770037931930041
                 e(r2) =  .0686191479801866
                  e(F) =  12.30755009394615
               e(df_r) =  2924
               e(df_m) =  17
                  e(N) =  2942

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.protransfer ib2.leftrightcat    i.east     i.female c.educyr    c.kr_inz_rate10 c.kr_tod_rate10    [pwe.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "robust"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "pweight"
            e(vcetype) : "Robust"

matrices:
                  e(b) :  1 x 25
                  e(V) :  25 x 25
       e(V_modelbased) :  25 x 25

functions:
             e(sample)   

. 
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==1
(sum of wgt is 2,892.37299981713)

Linear regression                               Number of obs     =      2,924
                                                F(17, 2906)       =      11.73
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0699
                                                Root MSE          =     .96732

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0033788   .0233298    -0.14   0.885    -.0491235    .0423658
       1.incloss |  -.1102151   .0660303    -1.67   0.095     -.239686    .0192558
       idtfamily |  -.0068219   .0231388    -0.29   0.768     -.052192    .0385483
1.childrenathome |   .0751062   .0556339     1.35   0.177    -.0339796     .184192
   1.riskgroup01 |  -.2328901   .0426926    -5.46   0.000    -.3166009   -.1491792
           1.old |  -.0050778   .0580416    -0.09   0.930    -.1188847    .1087291
         sttecon |   .1179512   .0313704     3.76   0.000     .0564408    .1794617
       sttfamily |   .0969073    .029402     3.30   0.001     .0392564    .1545582
     protransfer |  -.0215841   .0079366    -2.72   0.007     -.037146   -.0060222
                 |
    leftrightcat |
        1. left  |  -.0865702   .0541983    -1.60   0.110    -.1928412    .0197007
       3. right  |   .0808986   .0670085     1.21   0.227    -.0504904    .2122877
   4. DK and NR  |   .0048218    .052834     0.09   0.927    -.0987741    .1084178
                 |
          1.east |    .302603   .0464723     6.51   0.000      .211481    .3937251
        1.female |  -.1081332   .0418111    -2.59   0.010    -.1901156   -.0261509
          educyr |  -.0228993   .0080366    -2.85   0.004    -.0386573   -.0071413
   kr_inz_rate10 |  -.0035883   .0037175    -0.97   0.335    -.0108776     .003701
   kr_tod_rate10 |   .0246682   .0190721     1.29   0.196     -.012728    .0620644
           _cons |  -.2628025   .1716603    -1.53   0.126    -.5993906    .0737856
----------------------------------------------------------------------------------

.    
. estimates store Novemberwithout

. ereturn list

scalars:
               e(rank) =  18
               e(ll_0) =  -4148.69102506701
                 e(ll) =  -4042.80247601734
               e(r2_a) =  .0644252822643079
                e(rss) =  2719.179681224013
                e(mss) =  204.2498823788783
               e(rmse) =  .9673221850260133
                 e(r2) =  .0698665310503177
                  e(F) =  11.72784639786629
               e(df_r) =  2906
               e(df_m) =  17
                  e(N) =  2924

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.protransfer ib2.leftrightcat    i.east     i.female c.educyr    c.kr_inz_rate10 c.kr_tod_rate10    [pwe.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "robust"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "pweight"
            e(vcetype) : "Robust"

matrices:
                  e(b) :  1 x 25
                  e(V) :  25 x 25
       e(V_modelbased) :  25 x 25

functions:
             e(sample)   

. 
. 
. 
. coefplot (May, msymb(o) mcolor(blue) ciopts(lcolor(blue)) drop(_cons) ) ///
>     (November, msymb(t) mcolor(red*1.2) ciopts(lcolor(red*1.2)) drop(_cons) ) ///
>         (Maywithout, msymb(x) mcolor(blue) ciopts(lcolor(blue)) drop(_cons) ) ///
>         (Novemberwithout, msymb(d) mcolor(red*1.2) ciopts(lcolor(red*1.2)) drop(_cons) ) ///
>         , ///
>         xline(0, lwidth(medthin) lcolor(black)) ///
>     headings(idtecon = "{bf:Individual threat:}" ///
>                  sttecon = "{bf:Sociotropic threat:}" ///
>                          doubtstate = "{bf:Political orientations:}" ///
>                          1.east = "{bf:Control variables:}" ///
>                          , labsize(2.5)) ///
>         coeflabels(idtfamily = "Family situation" ///
>                    1.childrenathome = "Children in household" ///
>                            idtecon = "Economic situation" ///
>                            1.incloss = "Income loss in pandemic" ///
>                            1.riskgroup01 = "Covid-19 at-risk group" ///
>                            badhealth = "Bad health" ///
>                            1.old = "Age >70" ///
>                            sttfamily = "Family situation" ///
>                            sttecon = "Economic situation" ///
>                            doubtstate = "Low trust in institutions" ///
>                            protransfer = "Pro redistribution" ///
>                            1.leftrightcat = "Pol. ideol.: left-wing" ///
>                            3.leftrightcat = "Pol. ideol.: right-wing" ///
>                            4.leftrightcat = "Pol. ideol.: missing" ///
>                            gentrust = "General trust" ///
>                            1.east = "East Germany" ///
>                            1.female = "Gender female" ///
>                            educyr = "Education (years)" ///
>                            kr_inz_rate10 = "Infections in last 7 days/10K pop." ///
>                            kr_tod_rate10 = "Cumulated deaths/10K pop." ///
>                            , labsize(2.5)) ///
>         xlabel(-.4(.1).4, labsize(2.2)) ylabel(, labsize(2.5)) ///
>         plotlabels("May" "November" "May without trust" "November without trust") ///
>         graphregion(fcolor(gs15)) ///
>     xtitle("Effect on opposition to containments", size(2.5)) legend( size(vsmall)) ///
>         ysize(15) xsize(10)

.         
.         
. *Investigate suppression effect of general trust
. reg  sttecon c.gentrust ///
>    [pweight=persweight] 
(sum of wgt is 6,432.35599797964)

Linear regression                               Number of obs     =      6,438
                                                F(1, 6436)        =     113.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0237
                                                Root MSE          =     .85169

------------------------------------------------------------------------------
             |               Robust
     sttecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    gentrust |  -.0519263   .0048715   -10.66   0.000    -.0614761   -.0423765
       _cons |   4.043647   .0232065   174.25   0.000     3.998155     4.08914
------------------------------------------------------------------------------

.    
. reg  sttfamily c.gentrust ///
>    [pweight=persweight]    
(sum of wgt is 6,397.03599795699)

Linear regression                               Number of obs     =      6,400
                                                F(1, 6398)        =      81.22
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0172
                                                Root MSE          =     .93647

------------------------------------------------------------------------------
             |               Robust
   sttfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    gentrust |  -.0485323   .0053852    -9.01   0.000    -.0590892   -.0379754
       _cons |   3.769155   .0262539   143.57   0.000     3.717689    3.820621
------------------------------------------------------------------------------

. 
. mlogit leftrightcat c.gentrust ///
>    [pweight=persweight]    

Iteration 0:   log pseudolikelihood = -8132.5362  
Iteration 1:   log pseudolikelihood = -8095.1926  
Iteration 2:   log pseudolikelihood =  -8095.112  
Iteration 3:   log pseudolikelihood =  -8095.112  

Multinomial logistic regression                 Number of obs     =      6,425
                                                Wald chi2(3)      =      54.86
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -8095.112               Pseudo R2         =     0.0046

------------------------------------------------------------------------------
             |               Robust
leftrightcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1__left      |
    gentrust |   .0216249   .0153083     1.41   0.158    -.0083789    .0516286
       _cons |  -1.030971    .078364   -13.16   0.000    -1.184561   -.8773802
-------------+----------------------------------------------------------------
2__mid       |  (base outcome)
-------------+----------------------------------------------------------------
3__right     |
    gentrust |  -.0570786     .01857    -3.07   0.002     -.093475   -.0206821
       _cons |  -1.043402   .0865176   -12.06   0.000    -1.212973   -.8738307
-------------+----------------------------------------------------------------
4__DK_and_NR |
    gentrust |  -.0910491    .014487    -6.28   0.000     -.119443   -.0626552
       _cons |   -.327109    .067545    -4.84   0.000    -.4594946   -.1947233
------------------------------------------------------------------------------

.    
.    
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==0
(sum of wgt is 2,917.46899853647)

Linear regression                               Number of obs     =      2,942
                                                F(17, 2924)       =      12.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0686
                                                Root MSE          =       .977

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0226518   .0239407    -0.95   0.344    -.0695943    .0242906
       1.incloss |   .0390559   .0639661     0.61   0.542    -.0863671     .164479
       idtfamily |   .0162481   .0241365     0.67   0.501    -.0310782    .0635744
1.childrenathome |   .0075456   .0532326     0.14   0.887    -.0968317    .1119228
   1.riskgroup01 |  -.3633445   .0441644    -8.23   0.000    -.4499411    -.276748
           1.old |  -.0898714    .060463    -1.49   0.137    -.2084258    .0286831
         sttecon |   .0542598   .0313387     1.73   0.083    -.0071883    .1157079
       sttfamily |   .0919794   .0280242     3.28   0.001     .0370303    .1469285
     protransfer |  -.0080454   .0083418    -0.96   0.335    -.0244018     .008311
                 |
    leftrightcat |
        1. left  |  -.1434072    .054746    -2.62   0.009    -.2507518   -.0360627
       3. right  |   .0401225   .0724634     0.55   0.580    -.1019621     .182207
   4. DK and NR  |  -.0411136     .05439    -0.76   0.450    -.1477602    .0655329
                 |
          1.east |   .3540778   .0450851     7.85   0.000     .2656759    .4424796
        1.female |  -.0911737   .0419267    -2.17   0.030    -.1733826   -.0089647
          educyr |  -.0077095   .0079679    -0.97   0.333    -.0233327    .0079137
   kr_inz_rate10 |   .0036011   .0372934     0.10   0.923    -.0695229    .0767252
   kr_tod_rate10 |   .0152156   .0230733     0.66   0.510    -.0300259    .0604571
           _cons |  -.2216718   .1744935    -1.27   0.204    -.5638144    .1204707
----------------------------------------------------------------------------------

.    
.    
. *Check effects of self-interest variables if introduced separately along with
. *  non-selfinterest variables into the modelsample1
. *Re-run main model for May
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==0
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(19, 2915)       =      18.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1151
                                                Root MSE          =     .95313

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0387569   .0236479    -1.64   0.101    -.0851252    .0076114
       1.incloss |   .0411791   .0620902     0.66   0.507     -.080566    .1629241
       idtfamily |  -.0151395   .0237214    -0.64   0.523     -.061652     .031373
1.childrenathome |  -.0126736   .0512708    -0.25   0.805    -.1132042     .087857
   1.riskgroup01 |  -.3525135   .0431559    -8.17   0.000    -.4371326   -.2678944
           1.old |  -.0691034   .0607612    -1.14   0.256    -.1882427    .0500359
         sttecon |   .0477287   .0306149     1.56   0.119    -.0123003    .1077577
       sttfamily |   .0850086    .027782     3.06   0.002     .0305343     .139483
      doubtstate |   .1830349   .0186037     9.84   0.000     .1465572    .2195125
     protransfer |    -.01333   .0081913    -1.63   0.104    -.0293914    .0027314
                 |
    leftrightcat |
        1. left  |  -.1259192    .054463    -2.31   0.021    -.2327092   -.0191293
       3. right  |  -.0182548   .0697313    -0.26   0.794    -.1549824    .1184729
   4. DK and NR  |  -.0548026    .053331    -1.03   0.304    -.1593729    .0497677
                 |
          1.east |   .3038457   .0436968     6.95   0.000     .2181659    .3895255
        gentrust |   .0386436   .0093263     4.14   0.000     .0203568    .0569304
        1.female |  -.0797826    .040936    -1.95   0.051     -.160049    .0004838
          educyr |   .0014154   .0078617     0.18   0.857    -.0139996    .0168303
   kr_inz_rate10 |  -.0095933   .0365551    -0.26   0.793    -.0812698    .0620832
   kr_tod_rate10 |   .0125349   .0214855     0.58   0.560    -.0295934    .0546631
           _cons |  -.9662276   .1852152    -5.22   0.000    -1.329394   -.6030618
----------------------------------------------------------------------------------

. 
. gen modelsample3 = e(sample)

. 
. *Run regressions for May
. reg zopenindex c.idtecon c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==0 & modelsample3==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(14, 2920)       =      16.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0840
                                                Root MSE          =     .96891

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
      idtecon |  -.0304381   .0205068    -1.48   0.138    -.0706474    .0097712
      sttecon |   .0448551   .0309475     1.45   0.147     -.015826    .1055362
    sttfamily |   .0775155    .027499     2.82   0.005      .023596    .1314349
   doubtstate |   .1875551   .0185788    10.10   0.000     .1511262     .223984
  protransfer |  -.0148536   .0083345    -1.78   0.075    -.0311957    .0014885
              |
 leftrightcat |
     1. left  |  -.1272021   .0545178    -2.33   0.020    -.2340994   -.0203047
    3. right  |  -.0240774   .0703957    -0.34   0.732    -.1621076    .1139528
4. DK and NR  |  -.0604136   .0545491    -1.11   0.268    -.1673722    .0465451
              |
       1.east |   .2922725    .044598     6.55   0.000     .2048259    .3797192
     gentrust |   .0363068   .0093861     3.87   0.000     .0179027    .0547109
     1.female |   -.097571   .0413874    -2.36   0.018    -.1787225   -.0164195
       educyr |   .0122134   .0078423     1.56   0.119    -.0031636    .0275903
kr_inz_rate10 |  -.0110676   .0361162    -0.31   0.759    -.0818834    .0597483
kr_tod_rate10 |   .0161709   .0224727     0.72   0.472     -.027893    .0602347
        _cons |  -1.257909   .1839905    -6.84   0.000    -1.618673   -.8971446
-------------------------------------------------------------------------------

. reg zopenindex i.incloss c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==0 & modelsample3==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(14, 2920)       =      16.79
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0833
                                                Root MSE          =     .96931

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    1.incloss |   .0460485   .0587563     0.78   0.433    -.0691595    .1612565
      sttecon |    .033739    .030489     1.11   0.269    -.0260432    .0935212
    sttfamily |   .0738122   .0274767     2.69   0.007     .0199366    .1276878
   doubtstate |   .1820879   .0184707     9.86   0.000     .1458709    .2183048
  protransfer |   -.016852   .0082791    -2.04   0.042    -.0330856   -.0006185
              |
 leftrightcat |
     1. left  |  -.1276954   .0547032    -2.33   0.020    -.2349562   -.0204346
    3. right  |  -.0270016   .0701457    -0.38   0.700    -.1645416    .1105383
4. DK and NR  |  -.0652768   .0546896    -1.19   0.233    -.1725109    .0419572
              |
       1.east |   .2939117   .0446511     6.58   0.000     .2063608    .3814625
     gentrust |    .036968   .0093799     3.94   0.000     .0185762    .0553599
     1.female |  -.0904723   .0414647    -2.18   0.029    -.1717753   -.0091694
       educyr |   .0125292   .0078614     1.59   0.111    -.0028852    .0279437
kr_inz_rate10 |  -.0134306   .0362414    -0.37   0.711    -.0844919    .0576307
kr_tod_rate10 |   .0153349   .0224831     0.68   0.495    -.0287495    .0594193
        _cons |  -1.260798    .184281    -6.84   0.000    -1.622132   -.8994639
-------------------------------------------------------------------------------

. reg zopenindex c.idtfamily c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==0 & modelsample3==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(14, 2920)       =      16.95
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0843
                                                Root MSE          =     .96878

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    idtfamily |  -.0355708   .0217162    -1.64   0.102    -.0781514    .0070098
      sttecon |   .0380251     .03036     1.25   0.210    -.0215041    .0975543
    sttfamily |   .0819271   .0277594     2.95   0.003     .0274971    .1363572
   doubtstate |   .1889113   .0187635    10.07   0.000     .1521203    .2257023
  protransfer |  -.0147354    .008303    -1.77   0.076    -.0310157     .001545
              |
 leftrightcat |
     1. left  |  -.1305979   .0546643    -2.39   0.017    -.2377824   -.0234134
    3. right  |  -.0226131    .070105    -0.32   0.747    -.1600734    .1148472
4. DK and NR  |  -.0618909   .0547056    -1.13   0.258    -.1691565    .0453747
              |
       1.east |   .2925376   .0445754     6.56   0.000     .2051351      .37994
     gentrust |   .0369173    .009388     3.93   0.000     .0185096    .0553251
     1.female |  -.0963928   .0414772    -2.32   0.020    -.1777202   -.0150653
       educyr |   .0124014   .0078405     1.58   0.114     -.002972    .0277748
kr_inz_rate10 |  -.0116851   .0358279    -0.33   0.744    -.0819357    .0585654
kr_tod_rate10 |   .0148744   .0225304     0.66   0.509    -.0293027    .0590515
        _cons |  -1.254214   .1843631    -6.80   0.000    -1.615709   -.8927188
-------------------------------------------------------------------------------

. reg zopenindex i.childrenathome c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==0 & modelsample3==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(14, 2920)       =      16.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0837
                                                Root MSE          =     .96909

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
1.childrenathome |   .0673366   .0497946     1.35   0.176    -.0302996    .1649727
         sttecon |   .0374636   .0304352     1.23   0.218     -.022213    .0971401
       sttfamily |   .0703304   .0275851     2.55   0.011     .0162423    .1244186
      doubtstate |   .1812463   .0184407     9.83   0.000     .1450882    .2174045
     protransfer |  -.0169256   .0082872    -2.04   0.041    -.0331749   -.0006762
                 |
    leftrightcat |
        1. left  |  -.1245754   .0549625    -2.27   0.023    -.2323445   -.0168063
       3. right  |   -.027775    .070129    -0.40   0.692    -.1652824    .1097324
   4. DK and NR  |  -.0623631   .0546759    -1.14   0.254    -.1695704    .0448442
                 |
          1.east |   .2961134   .0446744     6.63   0.000     .2085168      .38371
        gentrust |   .0371937   .0094065     3.95   0.000     .0187496    .0556377
        1.female |  -.0955785   .0415455    -2.30   0.021      -.17704    -.014117
          educyr |   .0120399   .0078955     1.52   0.127    -.0034414    .0275212
   kr_inz_rate10 |  -.0125511   .0362504    -0.35   0.729    -.0836301    .0585278
   kr_tod_rate10 |   .0152263   .0225387     0.68   0.499    -.0289671    .0594196
           _cons |  -1.258438   .1843094    -6.83   0.000    -1.619827   -.8970483
----------------------------------------------------------------------------------

. reg zopenindex i.riskgroup01 c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==0 & modelsample3==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(14, 2920)       =      24.39
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1127
                                                Root MSE          =     .95365

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.riskgroup01 |  -.3630815   .0412969    -8.79   0.000    -.4440554   -.2821076
      sttecon |   .0378663   .0301378     1.26   0.209    -.0212271    .0969597
    sttfamily |   .0760594   .0273861     2.78   0.006     .0223614    .1297574
   doubtstate |   .1765931   .0182656     9.67   0.000     .1407783    .2124079
  protransfer |  -.0159829   .0081476    -1.96   0.050    -.0319585   -7.31e-06
              |
 leftrightcat |
     1. left  |  -.1254029   .0544355    -2.30   0.021    -.2321387   -.0186671
    3. right  |  -.0284972   .0700917    -0.41   0.684    -.1659314     .108937
4. DK and NR  |  -.0587138   .0532334    -1.10   0.270    -.1630927    .0456651
              |
       1.east |    .306715    .043768     7.01   0.000     .2208958    .3925342
     gentrust |   .0382918    .009275     4.13   0.000     .0201057    .0564779
     1.female |   -.077617   .0410471    -1.89   0.059    -.1581011    .0028672
       educyr |   .0028988   .0078815     0.37   0.713    -.0125551    .0183526
kr_inz_rate10 |  -.0122604     .03699    -0.33   0.740    -.0847895    .0602688
kr_tod_rate10 |   .0131748   .0215627     0.61   0.541    -.0291049    .0554545
        _cons |   -1.00524   .1852575    -5.43   0.000    -1.368489   -.6419913
-------------------------------------------------------------------------------

. reg zopenindex i.old c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==0 & modelsample3==1)
(sum of wgt is 2,911.36999846995)

Linear regression                               Number of obs     =      2,935
                                                F(14, 2920)       =      17.36
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0862
                                                Root MSE          =     .96776

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        1.old |  -.1599524   .0589742    -2.71   0.007    -.2755877   -.0443171
      sttecon |   .0365438   .0305361     1.20   0.232    -.0233308    .0964183
    sttfamily |   .0725582   .0273901     2.65   0.008     .0188523    .1262642
   doubtstate |    .178547   .0184789     9.66   0.000      .142314      .21478
  protransfer |  -.0176971   .0082287    -2.15   0.032    -.0338318   -.0015624
              |
 leftrightcat |
     1. left  |  -.1236754   .0546299    -2.26   0.024    -.2307925   -.0165583
    3. right  |  -.0178743   .0692656    -0.26   0.796    -.1536887    .1179401
4. DK and NR  |  -.0656715   .0546594    -1.20   0.230    -.1728463    .0415033
              |
       1.east |   .2970277   .0443922     6.69   0.000     .2099845    .3840708
     gentrust |   .0386913   .0094169     4.11   0.000     .0202269    .0571557
     1.female |  -.0886953   .0414882    -2.14   0.033    -.1700444   -.0073461
       educyr |   .0097966   .0078714     1.24   0.213    -.0056375    .0252307
kr_inz_rate10 |  -.0133386    .036662    -0.36   0.716    -.0852246    .0585473
kr_tod_rate10 |    .014739   .0222862     0.66   0.508    -.0289593    .0584373
        _cons |  -1.190972   .1841282    -6.47   0.000    -1.552007   -.8299382
-------------------------------------------------------------------------------

.    
. *Re-run main model for November
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if time==1
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(19, 2899)       =      17.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1267
                                                Root MSE          =     .93681

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0215578   .0225017    -0.96   0.338    -.0656788    .0225632
       1.incloss |  -.0992317    .062115    -1.60   0.110    -.2210258    .0225623
       idtfamily |  -.0246457   .0224904    -1.10   0.273    -.0687444    .0194531
1.childrenathome |   .0544339   .0528406     1.03   0.303    -.0491749    .1580428
   1.riskgroup01 |  -.1933677   .0419116    -4.61   0.000    -.2755472   -.1111882
           1.old |     .01391    .057794     0.24   0.810    -.0994116    .1272315
         sttecon |    .072711   .0306416     2.37   0.018     .0126294    .1327925
       sttfamily |   .0723234   .0285233     2.54   0.011     .0163954    .1282513
      doubtstate |   .1989073   .0188034    10.58   0.000      .162038    .2357766
     protransfer |  -.0253399   .0075696    -3.35   0.001    -.0401822   -.0104976
                 |
    leftrightcat |
        1. left  |  -.0547614   .0525002    -1.04   0.297    -.1577029    .0481801
       3. right  |   .0421067   .0641434     0.66   0.512    -.0836645    .1678779
   4. DK and NR  |   -.012234   .0521515    -0.23   0.815    -.1144917    .0900237
                 |
          1.east |   .2531371   .0455845     5.55   0.000     .1637558    .3425183
        gentrust |   .0464572   .0082817     5.61   0.000     .0302186    .0626957
        1.female |   -.099572   .0406831    -2.45   0.014    -.1793428   -.0198012
          educyr |  -.0109418   .0078301    -1.40   0.162     -.026295    .0044114
   kr_inz_rate10 |  -.0037708   .0035924    -1.05   0.294    -.0108147     .003273
   kr_tod_rate10 |   .0155799   .0180671     0.86   0.389    -.0198457    .0510056
           _cons |  -.9491665   .1762996    -5.38   0.000    -1.294852   -.6034813
----------------------------------------------------------------------------------

.    
. gen modelsample4 = e(sample)

. 
. *Run regressions for November
. reg zopenindex c.idtecon c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==1 & modelsample4==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(14, 2904)       =      21.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1162
                                                Root MSE          =     .94163

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
      idtecon |  -.0412441   .0198316    -2.08   0.038    -.0801295   -.0023587
      sttecon |   .0781563    .030346     2.58   0.010     .0186545    .1376581
    sttfamily |   .0676675   .0269625     2.51   0.012     .0147999    .1205351
   doubtstate |   .2052573   .0187366    10.95   0.000     .1685189    .2419958
  protransfer |  -.0252273    .007667    -3.29   0.001    -.0402607   -.0101939
              |
 leftrightcat |
     1. left  |  -.0574891   .0527857    -1.09   0.276    -.1609903    .0460122
    3. right  |   .0416363   .0646642     0.64   0.520     -.085156    .1684287
4. DK and NR  |  -.0168069   .0522372    -0.32   0.748    -.1192327    .0856189
              |
       1.east |   .2457569   .0458607     5.36   0.000     .1558341    .3356798
     gentrust |   .0487066   .0082589     5.90   0.000     .0325127    .0649005
     1.female |  -.1011075   .0409295    -2.47   0.014    -.1813612   -.0208538
       educyr |  -.0052607   .0076812    -0.68   0.493    -.0203219    .0098005
kr_inz_rate10 |  -.0036368   .0036078    -1.01   0.314     -.010711    .0034374
kr_tod_rate10 |   .0154624   .0179396     0.86   0.389    -.0197132     .050638
        _cons |  -1.147238   .1710227    -6.71   0.000    -1.482576   -.8119002
-------------------------------------------------------------------------------

. reg zopenindex i.incloss c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==1 & modelsample4==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(14, 2904)       =      21.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1155
                                                Root MSE          =     .94196

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    1.incloss |  -.1080109   .0584064    -1.85   0.065    -.2225331    .0065114
      sttecon |   .0711032   .0297757     2.39   0.017     .0127196    .1294867
    sttfamily |   .0592493   .0268705     2.20   0.028     .0065622    .1119365
   doubtstate |   .2002954   .0184092    10.88   0.000     .1641989    .2363919
  protransfer |  -.0270833   .0076018    -3.56   0.000    -.0419888   -.0121779
              |
 leftrightcat |
     1. left  |  -.0525691   .0527168    -1.00   0.319    -.1559353    .0507971
    3. right  |   .0463828   .0648847     0.71   0.475    -.0808419    .1736075
4. DK and NR  |  -.0162028   .0522084    -0.31   0.756    -.1185721    .0861665
              |
       1.east |   .2478196   .0458499     5.41   0.000     .1579181    .3377211
     gentrust |   .0494268   .0082566     5.99   0.000     .0332375    .0656161
     1.female |  -.0974815   .0407229    -2.39   0.017    -.1773302   -.0176328
       educyr |  -.0037537    .007625    -0.49   0.623    -.0187046    .0111972
kr_inz_rate10 |  -.0035213   .0035985    -0.98   0.328    -.0105771    .0035346
kr_tod_rate10 |   .0181562   .0181001     1.00   0.316    -.0173341    .0536465
        _cons |  -1.188017   .1706923    -6.96   0.000    -1.522707   -.8533263
-------------------------------------------------------------------------------

. reg zopenindex c.idtfamily c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==1 & modelsample4==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(14, 2904)       =      21.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1160
                                                Root MSE          =     .94172

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    idtfamily |   -.040758   .0211298    -1.93   0.054    -.0821889     .000673
      sttecon |   .0652958   .0294713     2.22   0.027      .007509    .1230827
    sttfamily |   .0776811   .0280903     2.77   0.006     .0226023      .13276
   doubtstate |   .2045818   .0186385    10.98   0.000     .1680359    .2411278
  protransfer |  -.0259475    .007599    -3.41   0.001    -.0408476   -.0110475
              |
 leftrightcat |
     1. left  |  -.0561436   .0527226    -1.06   0.287     -.159521    .0472339
    3. right  |    .039213   .0647624     0.61   0.545    -.0877719    .1661979
4. DK and NR  |  -.0198164   .0522609    -0.38   0.705    -.1222887    .0826558
              |
       1.east |   .2498604   .0458613     5.45   0.000     .1599364    .3397845
     gentrust |   .0488976   .0082564     5.92   0.000     .0327085    .0650867
     1.female |   -.096782   .0407216    -2.38   0.018    -.1766282   -.0169359
       educyr |  -.0045938   .0076448    -0.60   0.548    -.0195836     .010396
kr_inz_rate10 |  -.0037173   .0036056    -1.03   0.303    -.0107871    .0033524
kr_tod_rate10 |   .0166388      .0179     0.93   0.353    -.0184592    .0517368
        _cons |  -1.152012     .17126    -6.73   0.000    -1.487816   -.8162089
-------------------------------------------------------------------------------

. reg zopenindex i.childrenathome c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==1 & modelsample4==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(14, 2904)       =      22.00
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1152
                                                Root MSE          =     .94213

----------------------------------------------------------------------------------
                 |               Robust
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
1.childrenathome |   .0833342   .0521979     1.60   0.110    -.0190144    .1856829
         sttecon |   .0643635   .0294235     2.19   0.029     .0066705    .1220564
       sttfamily |   .0583332    .026836     2.17   0.030     .0057136    .1109528
      doubtstate |   .1969687   .0184518    10.67   0.000     .1607888    .2331487
     protransfer |  -.0276494   .0075959    -3.64   0.000    -.0425432   -.0127556
                 |
    leftrightcat |
        1. left  |  -.0495428     .05283    -0.94   0.348    -.1531308    .0540452
       3. right  |    .044935   .0649212     0.69   0.489    -.0823613    .1722313
   4. DK and NR  |  -.0154168   .0522726    -0.29   0.768     -.117912    .0870784
                 |
          1.east |   .2496376   .0459729     5.43   0.000     .1594948    .3397803
        gentrust |   .0501326   .0082353     6.09   0.000      .033985    .0662802
        1.female |  -.0971895   .0407504    -2.38   0.017    -.1770921   -.0172869
          educyr |  -.0050069   .0076802    -0.65   0.515    -.0200661    .0100524
   kr_inz_rate10 |  -.0036599   .0036067    -1.01   0.310    -.0107318     .003412
   kr_tod_rate10 |   .0155376   .0179532     0.87   0.387    -.0196647    .0507399
           _cons |  -1.156072   .1710124    -6.76   0.000     -1.49139   -.8207543
----------------------------------------------------------------------------------

. reg zopenindex i.riskgroup01 c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==1 & modelsample4==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(14, 2904)       =      23.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1226
                                                Root MSE          =     .93818

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.riskgroup01 |  -.1909931   .0401369    -4.76   0.000    -.2696927   -.1122935
      sttecon |   .0549899   .0295006     1.86   0.062    -.0028544    .1128341
    sttfamily |   .0662784   .0267397     2.48   0.013     .0138477     .118709
   doubtstate |   .1919465   .0183245    10.47   0.000     .1560161    .2278768
  protransfer |  -.0277572   .0075427    -3.68   0.000    -.0425468   -.0129676
              |
 leftrightcat |
     1. left  |  -.0499293   .0526707    -0.95   0.343    -.1532051    .0533465
    3. right  |   .0442178   .0644301     0.69   0.493    -.0821155    .1705511
4. DK and NR  |  -.0141056   .0521331    -0.27   0.787    -.1163271     .088116
              |
       1.east |   .2525984   .0455142     5.55   0.000      .163355    .3418418
     gentrust |   .0472355   .0082168     5.75   0.000     .0311241    .0633468
     1.female |  -.0895863   .0406711    -2.20   0.028    -.1693334   -.0098392
       educyr |   -.009347   .0077373    -1.21   0.227    -.0245183    .0058242
kr_inz_rate10 |  -.0039086   .0035962    -1.09   0.277    -.0109601    .0031428
kr_tod_rate10 |   .0154076   .0178773     0.86   0.389    -.0196458    .0504611
        _cons |  -.9678075   .1744993    -5.55   0.000    -1.309963   -.6256525
-------------------------------------------------------------------------------

. reg zopenindex i.old c.sttecon c.sttfamily c.doubtstate c.protransfer ///
>    ib2.leftrightcat i.east c.gentrust i.female c.educyr c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [pweight=persweight] if (time==1 & modelsample4==1)
(sum of wgt is 2,886.84199979901)

Linear regression                               Number of obs     =      2,919
                                                F(14, 2904)       =      21.58
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1143
                                                Root MSE          =     .94263

-------------------------------------------------------------------------------
              |               Robust
   zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        1.old |  -.0264005   .0543634    -0.49   0.627    -.1329953    .0801943
      sttecon |   .0627318   .0294239     2.13   0.033      .005038    .1204256
    sttfamily |   .0627225   .0267194     2.35   0.019     .0103317    .1151133
   doubtstate |   .1981594   .0184635    10.73   0.000     .1619565    .2343623
  protransfer |   -.027691   .0075632    -3.66   0.000    -.0425207   -.0128612
              |
 leftrightcat |
     1. left  |  -.0514976   .0528516    -0.97   0.330    -.1551281    .0521329
    3. right  |   .0447661   .0648961     0.69   0.490     -.082481    .1720131
4. DK and NR  |  -.0181005   .0522415    -0.35   0.729    -.1205346    .0843335
              |
       1.east |   .2490915   .0458847     5.43   0.000     .1591215    .3390614
     gentrust |   .0498167   .0082789     6.02   0.000     .0335836    .0660498
     1.female |  -.0937322   .0409116    -2.29   0.022     -.173951   -.0135135
       educyr |  -.0043639   .0076697    -0.57   0.569    -.0194025    .0106747
kr_inz_rate10 |  -.0036948   .0036031    -1.03   0.305    -.0107596    .0033701
kr_tod_rate10 |   .0166518   .0179135     0.93   0.353    -.0184727    .0517763
        _cons |  -1.159465   .1714842    -6.76   0.000    -1.495708   -.8232221
-------------------------------------------------------------------------------

.    
.    
.    
. 
end of do-file

. do "robustness suest descriptives.do"

. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *
. * Diehl/Wolter: “ATTITUDES ABOUT CONTAINMENT MEASURES DURING
. *                THE 2020/2021 CORONAVIRUS PANDEMIC: 
. *                SELF-INTEREST, OR BROADER POLITICAL ORIENTATIONS?"
. *
. * Do-File for robustness checks of descriptive data analysis
. *
. * This version: 20210602
. *
. * Author: Felix Wolter, felix.wolter@uni-konstanz.de
. *
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Figure 2: Opposition against containment measures
. reg openkitas5 i.time##i.east [iweight=persweight] 

      Source |       SS           df       MS      Number of obs   =     6,445
-------------+----------------------------------   F(3, 6441)      =     26.32
       Model |  127.080588         3  42.3601959   Prob > F        =    0.0000
    Residual |  10368.2065     6,441     1.60972   R-squared       =    0.0121
-------------+----------------------------------   Adj R-squared   =    0.0118
       Total |  10495.2871     6,444  1.62869136   Root MSE        =    1.2687

----------------------------------------------------------------------------------
      openkitas5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.0463629   .0343537    -1.35   0.177    -.1137076    .0209819
          1.east |   .4191981   .0617769     6.79   0.000     .2980949    .5403013
                 |
       time#east |
november 2020#1  |   -.081466   .0876459    -0.93   0.353    -.2532811    .0903492
                 |
           _cons |   3.104903   .0241541   128.55   0.000     3.057553    3.152253
----------------------------------------------------------------------------------

. est sto opkitas

. suest opkitas , vce(cluster id)

Cluster adjusted results for opkitas

                                                Number of obs     =      6,450

                                     (Std. Err. adjusted for 3,806 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
mean             |
            time |
  november 2020  |  -.0463629   .0345299    -1.34   0.179    -.1140402    .0213145
          1.east |   .4191981   .0520107     8.06   0.000     .3172591    .5211372
                 |
       time#east |
november 2020#1  |   -.081466    .062361    -1.31   0.191    -.2036914    .0407594
                 |
           _cons |   3.104903   .0284309   109.21   0.000      3.04918    3.160627
-----------------+----------------------------------------------------------------
lnvar            |
           _cons |   .4759112   .0150852    31.55   0.000     .4463448    .5054776
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,450
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.104903   .0284309   109.21   0.000      3.04918    3.160627
          2  |   3.524101   .0435522    80.92   0.000     3.438741    3.609462
          3  |    3.05854   .0293146   104.34   0.000     3.001085    3.115996
          4  |   3.396273   .0421032    80.67   0.000     3.313752    3.478793
------------------------------------------------------------------------------

. estimates store opkitas

. 
. reg openschools5 i.time##i.east [iweight=persweight] 

      Source |       SS           df       MS      Number of obs   =     6,438
-------------+----------------------------------   F(3, 6434)      =     44.50
       Model |     214.882         3  71.6273332   Prob > F        =    0.0000
    Residual |  10357.2837     6,434  1.60977365   R-squared       =    0.0203
-------------+----------------------------------   Adj R-squared   =    0.0200
       Total |  10572.1657     6,437  1.64240573   Root MSE        =    1.2687

----------------------------------------------------------------------------------
    openschools5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.2432054   .0343708    -7.08   0.000    -.3105835   -.1758272
          1.east |   .3748539   .0619046     6.06   0.000     .2535003    .4962076
                 |
       time#east |
november 2020#1  |   .0133291    .087735     0.15   0.879    -.1586607    .1853188
                 |
           _cons |   3.287021   .0241849   135.91   0.000      3.23961    3.334431
----------------------------------------------------------------------------------

. est sto opschools

. suest opschools , vce(cluster id)

Cluster adjusted results for opschools

                                                Number of obs     =      6,441

                                     (Std. Err. adjusted for 3,804 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
mean             |
            time |
  november 2020  |  -.2432054   .0348122    -6.99   0.000     -.311436   -.1749748
          1.east |   .3748539   .0516814     7.25   0.000     .2735602    .4761477
                 |
       time#east |
november 2020#1  |   .0133291    .061986     0.22   0.830    -.1081612    .1348194
                 |
           _cons |   3.287021   .0282992   116.15   0.000     3.231555    3.342486
-----------------+----------------------------------------------------------------
lnvar            |
           _cons |   .4759896   .0148745    32.00   0.000     .4468362     .505143
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,441
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.287021   .0282992   116.15   0.000     3.231555    3.342486
          2  |   3.661875   .0432449    84.68   0.000     3.577116    3.746633
          3  |   3.043815   .0295615   102.97   0.000     2.985876    3.101755
          4  |   3.431998    .040832    84.05   0.000     3.351969    3.512028
------------------------------------------------------------------------------

. estimates store opschools

. 
. reg openrestau5 i.time##i.east [iweight=persweight] 

      Source |       SS           df       MS      Number of obs   =     6,440
-------------+----------------------------------   F(3, 6436)      =     37.18
       Model |  209.146947         3   69.715649   Prob > F        =    0.0000
    Residual |  12069.5138     6,436   1.8753129   R-squared       =    0.0170
-------------+----------------------------------   Adj R-squared   =    0.0167
       Total |  12278.6608     6,439  1.90692045   Root MSE        =    1.3694

----------------------------------------------------------------------------------
     openrestau5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.2643037   .0370989    -7.12   0.000    -.3370298   -.1915776
          1.east |   .3566713   .0666796     5.35   0.000      .225957    .4873855
                 |
       time#east |
november 2020#1  |  -.0711095   .0946057    -0.75   0.452    -.2565681    .1143492
                 |
           _cons |   3.211776   .0260847   123.13   0.000     3.160641     3.26291
----------------------------------------------------------------------------------

. est sto oprestau

. suest oprestau , vce(cluster id)

Cluster adjusted results for oprestau

                                                Number of obs     =      6,447

                                     (Std. Err. adjusted for 3,806 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
mean             |
            time |
  november 2020  |  -.2643037   .0373646    -7.07   0.000    -.3375369   -.1910705
          1.east |   .3566713   .0528032     6.75   0.000     .2531789    .4601637
                 |
       time#east |
november 2020#1  |  -.0711095   .0630127    -1.13   0.259    -.1946121    .0523932
                 |
           _cons |   3.211776   .0292527   109.79   0.000     3.154442     3.26911
-----------------+----------------------------------------------------------------
lnvar            |
           _cons |   .6286724   .0130133    48.31   0.000     .6031668    .6541779
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,447
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.211776   .0292527   109.79   0.000     3.154442     3.26911
          2  |   3.568447   .0439598    81.18   0.000     3.482288    3.654607
          3  |   2.947472   .0327658    89.96   0.000     2.883252    3.011692
          4  |   3.233034   .0482705    66.98   0.000     3.138425    3.327642
------------------------------------------------------------------------------

. estimates store oprestau

. 
. reg opencontact5 i.time##i.east [iweight=persweight] 

      Source |       SS           df       MS      Number of obs   =     6,441
-------------+----------------------------------   F(3, 6437)      =    130.22
       Model |  700.903948         3  233.634649   Prob > F        =    0.0000
    Residual |  11549.4737     6,437  1.79423237   R-squared       =    0.0572
-------------+----------------------------------   Adj R-squared   =    0.0568
       Total |  12250.3777     6,440  1.90223256   Root MSE        =    1.3395

----------------------------------------------------------------------------------
    opencontact5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.6076652   .0362787   -16.75   0.000    -.6787835   -.5365469
          1.east |   .3558058   .0652266     5.45   0.000       .22794    .4836716
                 |
       time#east |
november 2020#1  |   .0539491   .0926502     0.58   0.560    -.1276761    .2355744
                 |
           _cons |   3.198347   .0255106   125.37   0.000     3.148338    3.248357
----------------------------------------------------------------------------------

. est sto opcontact

. suest opcontact , vce(cluster id)

Cluster adjusted results for opcontact

                                                Number of obs     =      6,446

                                     (Std. Err. adjusted for 3,801 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
mean             |
            time |
  november 2020  |  -.6076652    .036416   -16.69   0.000    -.6790392   -.5362912
          1.east |   .3558058    .051868     6.86   0.000     .2541464    .4574651
                 |
       time#east |
november 2020#1  |   .0539491   .0651614     0.83   0.408     -.073765    .1816632
                 |
           _cons |   3.198347   .0287743   111.15   0.000     3.141951    3.254744
-----------------+----------------------------------------------------------------
lnvar            |
           _cons |   .5845603   .0136162    42.93   0.000     .5578731    .6112476
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,446
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.198347   .0287743   111.15   0.000     3.141951    3.254744
          2  |   3.554153   .0431547    82.36   0.000     3.469572    3.638735
          3  |   2.590682   .0320157    80.92   0.000     2.527933    2.653432
          4  |   3.000437   .0484444    61.94   0.000     2.905488    3.095386
------------------------------------------------------------------------------

. estimates store opcontact

. 
. reg openborders5 i.time##i.east [iweight=persweight] 

      Source |       SS           df       MS      Number of obs   =     6,438
-------------+----------------------------------   F(3, 6434)      =     26.52
       Model |  169.092073         3  56.3640242   Prob > F        =    0.0000
    Residual |  13675.7699     6,434  2.12554708   R-squared       =    0.0122
-------------+----------------------------------   Adj R-squared   =    0.0118
       Total |   13844.862     6,437  2.15082522   Root MSE        =    1.4579

----------------------------------------------------------------------------------
    openborders5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.3107624   .0395085    -7.87   0.000    -.3882121   -.2333126
          1.east |   .0806306   .0709851     1.14   0.256    -.0585238    .2197849
                 |
       time#east |
november 2020#1  |  -.0696629   .1007088    -0.69   0.489    -.2670857    .1277599
                 |
           _cons |   3.047968   .0278139   109.58   0.000     2.993444    3.102493
----------------------------------------------------------------------------------

. est sto opborders

. suest opborders , vce(cluster id)

Cluster adjusted results for opborders

                                                Number of obs     =      6,443

                                     (Std. Err. adjusted for 3,805 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
mean             |
            time |
  november 2020  |  -.3107624   .0392598    -7.92   0.000    -.3877102   -.2338146
          1.east |   .0806306   .0568706     1.42   0.156    -.0308338    .1920949
                 |
       time#east |
november 2020#1  |  -.0696629    .068628    -1.02   0.310    -.2041714    .0648455
                 |
           _cons |   3.047968   .0318977    95.55   0.000      2.98545    3.110487
-----------------+----------------------------------------------------------------
lnvar            |
           _cons |    .754018   .0123205    61.20   0.000     .7298702    .7781658
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,443
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.047968   .0318977    95.55   0.000      2.98545    3.110487
          2  |   3.128599   .0470829    66.45   0.000     3.036318     3.22088
          3  |   2.737206   .0345106    79.31   0.000     2.669566    2.804845
          4  |   2.748173   .0505381    54.38   0.000     2.649121    2.847226
------------------------------------------------------------------------------

. estimates store opborders

. 
. reg openevents5 i.time##i.east [iweight=persweight] 

      Source |       SS           df       MS      Number of obs   =     6,451
-------------+----------------------------------   F(3, 6447)      =     89.87
       Model |  430.692126         3  143.564042   Prob > F        =    0.0000
    Residual |  10299.8738     6,447  1.59762274   R-squared       =    0.0401
-------------+----------------------------------   Adj R-squared   =    0.0397
       Total |  10730.5659     6,450  1.66365363   Root MSE        =    1.2639

----------------------------------------------------------------------------------
     openevents5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |   -.468927    .034212   -13.71   0.000     -.535994   -.4018601
          1.east |   .2850994   .0615219     4.63   0.000     .1644962    .4057027
                 |
       time#east |
november 2020#1  |   .1312399   .0872782     1.50   0.133    -.0398544    .3023341
                 |
           _cons |   2.257636   .0240658    93.81   0.000     2.210459    2.304813
----------------------------------------------------------------------------------

. est sto opevents

. suest opevents , vce(cluster id)

Cluster adjusted results for opevents

                                                Number of obs     =      6,456

                                     (Std. Err. adjusted for 3,808 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
mean             |
            time |
  november 2020  |   -.468927   .0334748   -14.01   0.000    -.5345365   -.4033176
          1.east |   .2850994   .0542529     5.26   0.000     .1787658    .3914331
                 |
       time#east |
november 2020#1  |   .1312399   .0604435     2.17   0.030     .0127729    .2497069
                 |
           _cons |   2.257636    .029185    77.36   0.000     2.200435    2.314838
-----------------+----------------------------------------------------------------
lnvar            |
           _cons |   .4684671   .0204782    22.88   0.000     .4283305    .5086037
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,456
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   2.257636    .029185    77.36   0.000     2.200435    2.314838
          2  |   2.542735   .0457341    55.60   0.000     2.453098    2.632373
          3  |   1.788709    .027531    64.97   0.000     1.734749    1.842669
          4  |   2.205048   .0456945    48.26   0.000     2.115489    2.294608
------------------------------------------------------------------------------

. estimates store opevents

. 
. coefplot (opkitas, msymb(O) mcolor(green*1.1) ciopts(lcolor(green*1.1))) ///
>    (opschools, msymb(X) mcolor(gs12) ciopts(lcolor(gs12))) ///
>    (oprestau, msymb(T) mcolor(blue) ciopts(lcolor(blue))) ///
>    (opcontact, msymb(S) mcolor(red*1.5) ciopts(lcolor(red*1.5)) ) ///
>    (opborders, msymb(D) mcolor(yellow*1.8) ciopts(lcolor(yellow*1.8)) ) ///
>    opevents, msymb(+) mcolor(black) ciopts(lcolor(black))  /// 
>    coeflabels(1._at = "May 2020, West" ///
>               2._at = "May 2020, East" ///
>                           3._at = "November 2020, West" ///
>                           4._at = "November 2020, East", labsize(3)) ///
>    graphregion(fcolor(gs15)) ///
>    xlabel(1(1)4, labsize(2.5)) ///
>    xline(1(.5)4, lcolor(gs12) lwidth(vthin)) ///
>    legend(order(2 "Daycare" 4 "Schools" 6 "Restaurants/bars" ///
>                 8 "Contacts" 10 "Borders" 12 "Events") rows(2))

. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Figure 3: Individual threats by region and time
. reg idtecon i.time##i.east [iweight=persweight] 

      Source |       SS           df       MS      Number of obs   =     6,448
-------------+----------------------------------   F(3, 6444)      =     13.58
       Model |  58.2463397         3  19.4154466   Prob > F        =    0.0000
    Residual |  9211.72594     6,444  1.42950434   R-squared       =    0.0063
-------------+----------------------------------   Adj R-squared   =    0.0058
       Total |  9269.97228     6,447  1.43787378   Root MSE        =    1.1956

----------------------------------------------------------------------------------
         idtecon |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |   .1784554   .0323624     5.51   0.000     .1150144    .2418965
          1.east |  -.0367409   .0583115    -0.63   0.529    -.1510508     .077569
                 |
       time#east |
november 2020#1  |   .0660878   .0826726     0.80   0.424    -.0959779    .2281536
                 |
           _cons |   2.357223   .0227659   103.54   0.000     2.312594    2.401852
----------------------------------------------------------------------------------

. est sto idtecon

. suest idtecon , vce(cluster id)

Cluster adjusted results for idtecon

                                                Number of obs     =      6,451

                                     (Std. Err. adjusted for 3,806 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
mean             |
            time |
  november 2020  |   .1784554   .0271077     6.58   0.000     .1253252    .2315857
          1.east |  -.0367409   .0462521    -0.79   0.427    -.1273933    .0539115
                 |
       time#east |
november 2020#1  |   .0660878   .0486644     1.36   0.174    -.0292927    .1614684
                 |
           _cons |   2.357223   .0267248    88.20   0.000     2.304843    2.409603
-----------------+----------------------------------------------------------------
lnvar            |
           _cons |   .3573096   .0182481    19.58   0.000     .3215439    .3930753
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,451
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   2.357223   .0267248    88.20   0.000     2.304843    2.409603
          2  |   2.320482   .0377497    61.47   0.000     2.246494     2.39447
          3  |   2.535678   .0277756    91.29   0.000     2.481239    2.590118
          4  |   2.565025   .0416291    61.62   0.000     2.483434    2.646617
------------------------------------------------------------------------------

. estimates store idtecon

. 
. reg idtfamily i.time##i.east [iweight=persweight] 

      Source |       SS           df       MS      Number of obs   =     6,411
-------------+----------------------------------   F(3, 6407)      =     27.51
       Model |  105.780391         3  35.2601304   Prob > F        =    0.0000
    Residual |  8211.16134     6,407  1.28159222   R-squared       =    0.0127
-------------+----------------------------------   Adj R-squared   =    0.0123
       Total |  8316.94173     6,410  1.29749481   Root MSE        =    1.1321

----------------------------------------------------------------------------------
       idtfamily |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |    .228117   .0307351     7.42   0.000      .167866    .2883681
          1.east |   .0348125   .0553081     0.63   0.529    -.0736099    .1432349
                 |
       time#east |
november 2020#1  |   .1113762   .0784574     1.42   0.156    -.0424264    .2651789
                 |
           _cons |   2.053636   .0216338    94.93   0.000     2.011226    2.096046
----------------------------------------------------------------------------------

. est sto idtfamily

. suest idtfamily , vce(cluster id)

Cluster adjusted results for idtfamily

                                                Number of obs     =      6,416

                                     (Std. Err. adjusted for 3,800 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
mean             |
            time |
  november 2020  |    .228117   .0290069     7.86   0.000     .1712646    .2849694
          1.east |   .0348125   .0440065     0.79   0.429    -.0514386    .1210636
                 |
       time#east |
november 2020#1  |   .1113762   .0507844     2.19   0.028     .0118407    .2109118
                 |
           _cons |   2.053636   .0247795    82.88   0.000     2.005069    2.102203
-----------------+----------------------------------------------------------------
lnvar            |
           _cons |   .2481018   .0203357    12.20   0.000     .2082446     .287959
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,416
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   2.053636   .0247795    82.88   0.000     2.005069    2.102203
          2  |   2.088449   .0363668    57.43   0.000     2.017171    2.159726
          3  |   2.281753   .0267526    85.29   0.000     2.229319    2.334187
          4  |   2.427942   .0393875    61.64   0.000     2.350744     2.50514
------------------------------------------------------------------------------

. estimates store idtfamily

. 
. logit childrenathome i.time##i.east [iweight=persweight] 

Iteration 0:   log likelihood = -3062.0179  
Iteration 1:   log likelihood =  -3060.745  
Iteration 2:   log likelihood =  -3060.744  
Iteration 3:   log likelihood =  -3060.744  

Logistic regression                             Number of obs     =      6,375
                                                LR chi2(3)        =       2.55
                                                Prob > chi2       =     0.4667
Log likelihood =  -3060.744                     Pseudo R2         =     0.0004

----------------------------------------------------------------------------------
  childrenathome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.0689055   .0696452    -0.99   0.322    -.2054076    .0675965
          1.east |  -.1117938   .1274813    -0.88   0.381    -.3616524    .1380649
                 |
       time#east |
november 2020#1  |   .0067151   .1831266     0.04   0.971    -.3522065    .3656367
                 |
           _cons |  -1.422911    .048373   -29.42   0.000     -1.51772   -1.328101
----------------------------------------------------------------------------------

. est sto childrenathome

. suest childrenathome , vce(cluster id)

Cluster adjusted results for childrenathome

                                                Number of obs     =      6,375

                                     (Std. Err. adjusted for 3,787 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
childrenathome   |
            time |
  november 2020  |  -.0689055   .0471053    -1.46   0.144    -.1612301    .0234191
          1.east |  -.1117938   .0987105    -1.13   0.257    -.3052628    .0816753
                 |
       time#east |
november 2020#1  |   .0067151   .0806542     0.08   0.934    -.1513642    .1647944
                 |
           _cons |  -1.422911   .0556026   -25.59   0.000     -1.53189   -1.313932
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) expression(1/(1+(exp(-(predict(xb)))))) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,375
Model VCE    : Robust

Expression   : 1/(1+(exp(-(predict(xb)))))

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1942057   .0087012    22.32   0.000     .1771515    .2112598
          2  |   .1773064   .0118971    14.90   0.000     .1539884    .2006244
          3  |   .1836493    .008709    21.09   0.000     .1665799    .2007186
          4  |    .168416   .0118402    14.22   0.000     .1452098    .1916223
------------------------------------------------------------------------------

. estimates store childrenathome

. 
. logit incloss i.time##i.east [iweight=persweight] 

Iteration 0:   log likelihood = -2696.1445  
Iteration 1:   log likelihood = -2692.9259  
Iteration 2:   log likelihood = -2692.9185  
Iteration 3:   log likelihood = -2692.9185  

Logistic regression                             Number of obs     =      6,447
                                                LR chi2(3)        =       6.45
                                                Prob > chi2       =     0.0916
Log likelihood = -2692.9185                     Pseudo R2         =     0.0012

----------------------------------------------------------------------------------
         incloss |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.1468603   .0761954    -1.93   0.054    -.2962007      .00248
          1.east |  -.0285723   .1345015    -0.21   0.832    -.2921904    .2350457
                 |
       time#east |
november 2020#1  |  -.1160191   .1998193    -0.58   0.561    -.5076578    .2756196
                 |
           _cons |  -1.669661   .0522658   -31.95   0.000      -1.7721   -1.567222
----------------------------------------------------------------------------------

. est sto incloss

. suest incloss , vce(cluster id)

Cluster adjusted results for incloss

                                                Number of obs     =      6,447

                                     (Std. Err. adjusted for 3,807 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
incloss          |
            time |
  november 2020  |  -.1468603   .0644392    -2.28   0.023    -.2731588   -.0205619
          1.east |  -.0285723   .1061295    -0.27   0.788    -.2365823    .1794376
                 |
       time#east |
november 2020#1  |  -.1160191   .1104178    -1.05   0.293     -.332434    .1003959
                 |
           _cons |  -1.669661   .0610841   -27.33   0.000    -1.789383   -1.549938
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) expression(1/(1+(exp(-(predict(xb)))))) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,447
Model VCE    : Robust

Expression   : 1/(1+(exp(-(predict(xb)))))

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1584694    .008146    19.45   0.000     .1425036    .1744353
          2  |   .1546962   .0113489    13.63   0.000     .1324528    .1769396
          3  |   .1398518   .0078457    17.83   0.000     .1244746    .1552291
          4  |   .1233467   .0107248    11.50   0.000     .1023264     .144367
------------------------------------------------------------------------------

. estimates store incloss

. 
. logit riskgroup01 i.time##i.east [iweight=persweight] 

Iteration 0:   log likelihood = -4133.1603  
Iteration 1:   log likelihood = -4130.5466  
Iteration 2:   log likelihood = -4130.5464  

Logistic regression                             Number of obs     =      6,209
                                                LR chi2(3)        =       5.23
                                                Prob > chi2       =     0.1559
Log likelihood = -4130.5464                     Pseudo R2         =     0.0006

----------------------------------------------------------------------------------
     riskgroup01 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |   .0861091   .0568498     1.51   0.130    -.0253145    .1975327
          1.east |   .1481435   .1014294     1.46   0.144    -.0506545    .3469415
                 |
       time#east |
november 2020#1  |  -.0535458   .1431886    -0.37   0.708    -.3341902    .2270986
                 |
           _cons |  -.5070685   .0402015   -12.61   0.000    -.5858619   -.4282751
----------------------------------------------------------------------------------

. est sto riskgroup01

. suest riskgroup01 , vce(cluster id)

Cluster adjusted results for riskgroup01

                                                Number of obs     =      6,209

                                     (Std. Err. adjusted for 3,731 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
riskgroup01      |
            time |
  november 2020  |   .0861091   .0420086     2.05   0.040     .0037737    .1684445
          1.east |   .1481435   .0858744     1.73   0.085    -.0201673    .3164542
                 |
       time#east |
november 2020#1  |  -.0535458   .0741987    -0.72   0.471    -.1989726    .0918811
                 |
           _cons |  -.5070685   .0477633   -10.62   0.000    -.6006828   -.4134542
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) expression(1/(1+(exp(-(predict(xb)))))) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,209
Model VCE    : Robust

Expression   : 1/(1+(exp(-(predict(xb)))))

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .375881    .011205    33.55   0.000     .3539196    .3978424
          2  |   .4112198    .017279    23.80   0.000     .3773537     .445086
          3  |   .3962872   .0114011    34.76   0.000     .3739415    .4186329
          4  |   .4191261    .017278    24.26   0.000     .3852618    .4529904
------------------------------------------------------------------------------

. estimates store riskgroup01

. 
. logit old i.time##i.east [iweight=persweight] 

Iteration 0:   log likelihood = -2832.8764  
Iteration 1:   log likelihood = -2830.2542  
Iteration 2:   log likelihood = -2830.2441  
Iteration 3:   log likelihood = -2830.2441  

Logistic regression                             Number of obs     =      6,459
                                                LR chi2(3)        =       5.26
                                                Prob > chi2       =     0.1534
Log likelihood = -2830.2441                     Pseudo R2         =     0.0009

----------------------------------------------------------------------------------
             old |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
            time |
  november 2020  |  -.0276448   .0747476    -0.37   0.711    -.1741474    .1188577
          1.east |   .1474088   .1284003     1.15   0.251    -.1042512    .3990688
                 |
       time#east |
november 2020#1  |   .1091407   .1803785     0.61   0.545    -.2443947    .4626761
                 |
           _cons |  -1.681118   .0523322   -32.12   0.000    -1.783687   -1.578549
----------------------------------------------------------------------------------

. est sto old

. suest old , vce(cluster id)

Cluster adjusted results for old

                                                Number of obs     =      6,459

                                     (Std. Err. adjusted for 3,808 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
old              |
            time |
  november 2020  |  -.0276448   .0402817    -0.69   0.493    -.1065954    .0513058
          1.east |   .1474088   .1256762     1.17   0.241    -.0989121    .3937296
                 |
       time#east |
november 2020#1  |   .1091407   .0757483     1.44   0.150    -.0393233    .2576047
                 |
           _cons |  -1.681118   .0653717   -25.72   0.000    -1.809244   -1.552992
----------------------------------------------------------------------------------

. margins, at(time=(0 1) east=(0 1)) expression(1/(1+(exp(-(predict(xb)))))) post
Warning: cannot perform check for estimable functions.

Adjusted predictions                            Number of obs     =      6,459
Model VCE    : Robust

Expression   : 1/(1+(exp(-(predict(xb)))))

1._at        : time            =           0
               east            =           0

2._at        : time            =           0
               east            =           1

3._at        : time            =           1
               east            =           0

4._at        : time            =           1
               east            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1569475   .0086497    18.14   0.000     .1399945    .1739005
          2  |   .1774516   .0156671    11.33   0.000     .1467448    .2081585
          3  |   .1533242   .0087907    17.44   0.000     .1360948    .1705537
          4  |   .1896612    .015884    11.94   0.000     .1585292    .2207931
------------------------------------------------------------------------------

. estimates store old

. 
. 
. *Version 1 horizontally
. coefplot (idtecon, msymb(O) mcolor(red*1.5) ciopts(lcolor(red*1.5)))  ///
>    idtfamily, msymb(X) mcolor(blue) ciopts(lcolor(blue))  ///
>    coeflabels(1._at = "May 2020, West" ///
>               2._at = "May 2020, East" ///
>                           3._at = "November 2020, West" ///
>                           4._at = "November 2020, East", labsize(3.5)) ///
>    title("Continuous variables", size(4.5) color(black)) ///
>    graphregion(fcolor(gs15)) ///
>    xlabel(2(.5)3, labsize(2.5)) ///
>    xline(2(.2)3, lcolor(gs12) lwidth(vthin))  ///
>    legend(order(2 "Indiv. threat economic" 4 "Indiv. threat family") rows(2)) ///
>    saving(figure2a.gph, replace)
(file figure2a.gph saved)

. 
. 
. coefplot (childrenathome, msymb(T) mcolor(green*1.1) ciopts(lcolor(green*1.1))) ///
>    (incloss, msymb(S) mcolor(black) ciopts(lcolor(black))) ///
>    (riskgroup01, msymb(+) mcolor(yellow*1.8) ciopts(lcolor(yellow*1.8)))  ///
>    old, msymb(D) mcolor(gs12) ciopts(lcolor(gs12))  ///
>    coeflabels(, nolabels) ///
>    title("Binary variables", size(4.5) color(black)) ///
>    graphregion(fcolor(gs15)) ///
>    xlabel(0(.1).5, labsize(2.5)) ///
>    xline(0(.1).5, lcolor(gs12) lwidth(vthin))  ///
>    legend(order(2 "Children at home" 4 "Income loss" 6 "Covid-19 at-risk group" ///
>                 8 "Age >70") rows(2)) ///
>    saving(figure2b.gph, replace)
(file figure2b.gph saved)

.    
. graph combine "figure2a" "figure2b"

. 
. 
. 
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *Table 2
. 
. bysort time east: sum sttecon [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   2,258    2759.643    3.810834    .856456          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   1,000  498.332999    3.804881   .8854729          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   2,227    2704.269    3.834394    .858304          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |     973  491.992005    3.879164   .8833133          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |       0           0


. bysort time: sum sttecon [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   3,258    3257.976    3.809924    .860863          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
     sttecon |   3,200    3196.261    3.841286   .8622531          1          5


. 
. bysort time east: sum sttfamily [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   2,240    2738.395    3.546251   .9396018          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |     991  493.157999    3.558807   .9672946          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   2,220    2697.356    3.569247   .9403674          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |     969  490.008005    3.663728   .9625344          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |       0           0


. bysort time: sum sttfamily [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   3,231    3231.553    3.548167    .943784          1          5

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
   sttfamily |   3,189    3187.364    3.583772   .9443159          1          5


. 
. bysort time east: sum doubtstate [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   2,258    2759.643    3.507613   1.318255          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   1,000  498.332999    3.725783   1.397455          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   2,226    2702.962    3.476376   1.358789          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |     974  492.879005    3.686932   1.342688          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |       0           0


. bysort time: sum doubtstate [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   3,258    3257.976    3.540983   1.332838          1          7

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
  doubtstate |   3,200    3195.841    3.508849   1.358297          1          7


. 
. bysort time east: sum protransfer [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   2,243    2739.268    4.050865   2.703425          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |     989  492.366999    4.491745   2.722839          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   2,214    2686.558    3.966043   2.737881          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |     964  487.395005    4.321915   2.768971          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |       0           0


. bysort time: sum protransfer [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   3,232    3231.635    4.118037   2.710725          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
 protransfer |   3,178    3173.953    4.020691   2.745365          0         10


. 
. bysort time east: tab leftrightcat [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left | 494.174999       17.96       17.96
      2. mid |  1,279.148       46.49       64.45
    3. right |    325.879       11.84       76.30
4. DK and NR | 652.062999       23.70      100.00
-------------+-----------------------------------
       Total |  2,751.265      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |    103.046       20.76       20.76
      2. mid |230.7149992       46.48       67.24
    3. right |     66.998       13.50       80.74
4. DK and NR | 95.6249999       19.26      100.00
-------------+-----------------------------------
       Total | 496.383999      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |    470.022       17.41       17.41
      2. mid |   1,231.35       45.62       63.04
    3. right | 358.695999       13.29       76.33
4. DK and NR | 638.964999       23.67      100.00
-------------+-----------------------------------
       Total |  2,699.033      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left |94.50400123       19.21       19.21
      2. mid |227.6960021       46.29       65.50
    3. right | 71.9330007       14.62       80.13
4. DK and NR | 97.7480008       19.87      100.00
-------------+-----------------------------------
       Total | 491.881005      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations


. bysort time: tab leftrightcat [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left | 597.220999       18.39       18.39
      2. mid |  1,509.863       46.49       64.88
    3. right |    392.877       12.10       76.98
4. DK and NR | 747.687999       23.02      100.00
-------------+-----------------------------------
       Total |  3,247.649      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

  Left-Right |
 categorical |      Freq.     Percent        Cum.
-------------+-----------------------------------
     1. left | 564.526002       17.69       17.69
      2. mid |  1,459.046       45.73       63.42
    3. right |    430.629       13.50       76.91
4. DK and NR | 736.712999       23.09      100.00
-------------+-----------------------------------
       Total |  3,190.914      100.00


. 
. bysort time: tab east [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

       east |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  2,759.643       84.70       84.70
          1 | 498.332999       15.30      100.00
------------+-----------------------------------
      Total |  3,257.976      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

       east |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  2,704.269       84.58       84.58
          1 | 492.879005       15.42      100.00
------------+-----------------------------------
      Total |  3,197.148      100.00


. 
. bysort time east: sum gentrust [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   2,248    2747.689    4.151285   2.519669          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |     997  497.169999    4.135388   2.564668          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   2,221        2696    4.233903   2.580747          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |     973  492.384005    4.337495   2.584645          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |       0           0


. bysort time: sum gentrust [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   3,245    3244.859    4.148849   2.526338          0         10

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
    gentrust |   3,194    3188.384    4.249901   2.581329          0         10


. 
. bysort time east: tab female [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,354.578       49.09       49.09
          1 |  1,405.065       50.91      100.00
------------+-----------------------------------
      Total |  2,759.643      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 | 244.785998       49.12       49.12
          1 |    253.547       50.88      100.00
------------+-----------------------------------
      Total | 498.332999      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .
no observations

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,324.156       48.97       48.97
          1 |  1,380.113       51.03      100.00
------------+-----------------------------------
      Total |  2,704.269      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 | 239.944003       48.68       48.68
          1 | 252.935002       51.32      100.00
------------+-----------------------------------
      Total | 492.879005      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .
no observations


. bysort time: tab female [iweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,599.364       49.09       49.09
          1 |  1,658.612       50.91      100.00
------------+-----------------------------------
      Total |  3,257.976      100.00

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    1,564.1       48.92       48.92
          1 |  1,633.048       51.08      100.00
------------+-----------------------------------
      Total |  3,197.148      100.00


. 
. bysort time east: sum educyr [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   2,241     2737.88    13.57277   2.813111          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |     987  491.829999    13.87939   2.366146          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   2,213    2685.175    13.63528   2.745723          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |     971  491.528005    13.95482   2.326943          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |       0           0


. bysort time: sum educyr [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   3,228     3229.71    13.61946   2.751671          7         22

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
      educyr |   3,184    3176.703    13.68472   2.687411          7         22


. 
. bysort time east: sum kr_inz_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   2,213    2701.831    .5479565   .5943786          0       8.03

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |     991  493.625999    .2318989   .5189215          0       6.58

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   2,192    2659.595    14.52052   5.396691       1.83      38.64

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |     962  486.069005    8.914234   6.024722        1.5      36.57

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |       0           0


. bysort time: sum kr_inz_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   3,204    3195.457    .4991328   .5943816          0       8.03

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_inz_ra~10 |   3,154    3145.664    13.65423   5.859408        1.5      38.64


. 
. bysort time east: sum kr_tod_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   2,213    2701.831    1.136842   1.092035          0   10.04373

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |     991  493.625999    .4823094   .6878416          0   4.825561

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |       0           0

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 0

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   2,192    2659.595    1.670292    1.20086          0   20.26483

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = 1

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |     962  486.069005    .9870068   .9932571          0   5.420912

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020, east = .

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |       0           0


. bysort time: sum kr_tod_rate10 [aweight=persweight]

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = spring 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   3,204    3195.457    1.035731    1.06639          0   10.04373

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> time = november 2020

    Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
-------------+-----------------------------------------------------------------
kr_tod_ra~10 |   3,154    3145.664    1.564711   1.196835          0   20.26483


. 
. 
. 
. 
. 
end of do-file

. do "robustness suest regressions.do"

. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. *
. * Diehl/Wolter: “ATTITUDES ABOUT CONTAINMENT MEASURES DURING
. *                THE 2020/2021 CORONAVIRUS PANDEMIC: 
. *                SELF-INTEREST, OR BROADER POLITICAL ORIENTATIONS?"
. *
. * Do-File for robustness checks of regression analysis
. *
. * This version: 20210415
. *
. * Author: Felix Wolter, felix.wolter@uni-konstanz.de
. *
. *XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
. 
. 
. *Final full models, figure 3 and table S4 (supplement)
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [iweight=persweight] if time==0

      Source |       SS           df       MS      Number of obs   =     2,911
-------------+----------------------------------   F(19, 2891)     =     19.80
       Model |  341.809103        19  17.9899528   Prob > F        =    0.0000
    Residual |  2626.81803     2,891  .908619173   R-squared       =    0.1151
-------------+----------------------------------   Adj R-squared   =    0.1094
       Total |  2968.62713     2,910  1.02014678   Root MSE        =    .95315

----------------------------------------------------------------------------------
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0387569   .0200098    -1.94   0.053    -.0779917    .0004779
       1.incloss |   .0411791   .0520818     0.79   0.429    -.0609422    .1433004
       idtfamily |  -.0151395   .0197477    -0.77   0.443    -.0538606    .0235815
1.childrenathome |  -.0126736   .0467536    -0.27   0.786    -.1043474    .0790002
   1.riskgroup01 |  -.3525135   .0384488    -9.17   0.000    -.4279034   -.2771236
           1.old |  -.0691034   .0527845    -1.31   0.191    -.1726025    .0343958
         sttecon |   .0477287   .0254452     1.88   0.061    -.0021639    .0976213
       sttfamily |   .0850086    .023035     3.69   0.000     .0398419    .1301754
      doubtstate |   .1830349   .0150149    12.19   0.000     .1535939    .2124758
     protransfer |    -.01333   .0068752    -1.94   0.053    -.0268107    .0001507
                 |
    leftrightcat |
        1. left  |  -.1259192   .0489411    -2.57   0.010    -.2218822   -.0299563
       3. right  |  -.0182548    .057373    -0.32   0.750    -.1307508    .0942413
   4. DK and NR  |  -.0548026   .0471437    -1.16   0.245    -.1472413    .0376361
                 |
          1.east |   .3038457   .0511466     5.94   0.000     .2035582    .4041332
        gentrust |   .0386436   .0076234     5.07   0.000     .0236957    .0535914
        1.female |  -.0797826    .036707    -2.17   0.030    -.1517572    -.007808
          educyr |   .0014154   .0068895     0.21   0.837    -.0120935    .0149242
   kr_inz_rate10 |  -.0095933   .0314634    -0.30   0.760    -.0712862    .0520996
   kr_tod_rate10 |   .0125349   .0172619     0.73   0.468     -.021312    .0463817
           _cons |  -.9662276   .1517924    -6.37   0.000     -1.26386   -.6685954
----------------------------------------------------------------------------------

. 
. estimates store May

. ereturn list

scalars:
               e(rank) =  20
               e(ll_0) =  -4159.06214332684
                 e(ll) =  -3981.016123311802
               e(r2_a) =  .1094390363866728
                e(rss) =  2626.818028368011
                e(mss) =  341.8091030717742
               e(rmse) =  .953154184760086
                 e(r2) =  .1151404632302193
                  e(F) =  19.80175604802501
               e(df_r) =  2891
               e(df_m) =  19
                  e(N) =  2911

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtstate c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educyr    c.kr_inz_rat.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "ols"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "iweight"

matrices:
                  e(b) :  1 x 27
                  e(V) :  27 x 27

functions:
             e(sample)   

. 
. reg zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome  ///
>    i.riskgroup01 i.old ///
>    c.sttecon c.sttfamily ///
>    c.doubtstate c.protransfer ib2.leftrightcat ///
>    i.east  ///   
>    c.gentrust ///
>    i.female c.educyr ///
>    c.kr_inz_rate10 c.kr_tod_rate10 ///
>    [iweight=persweight] if time==1

      Source |       SS           df       MS      Number of obs   =     2,886
-------------+----------------------------------   F(19, 2866)     =     21.89
       Model |  365.009597        19  19.2110314   Prob > F        =    0.0000
    Residual |  2516.19277     2,866  .877945839   R-squared       =    0.1267
-------------+----------------------------------   Adj R-squared   =    0.1212
       Total |  2881.20237     2,885  .998683664   Root MSE        =    .93685

----------------------------------------------------------------------------------
      zopenindex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         idtecon |  -.0215578    .020082    -1.07   0.283    -.0609344    .0178188
       1.incloss |  -.0992317    .055674    -1.78   0.075    -.2083968    .0099333
       idtfamily |  -.0246457   .0193252    -1.28   0.202    -.0625384    .0132471
1.childrenathome |   .0544339   .0468205     1.16   0.245    -.0373714    .1462393
   1.riskgroup01 |  -.1933677    .037716    -5.13   0.000    -.2673209   -.1194145
           1.old |     .01391   .0521868     0.27   0.790    -.0884175    .1162375
         sttecon |    .072711   .0275032     2.64   0.008     .0187829     .126639
       sttfamily |   .0723234   .0249702     2.90   0.004      .023362    .1212847
      doubtstate |   .1989073    .015138    13.14   0.000     .1692248    .2285899
     protransfer |  -.0253399   .0065798    -3.85   0.000    -.0382414   -.0124383
                 |
    leftrightcat |
        1. left  |  -.0547614   .0490554    -1.12   0.264    -.1509489     .041426
       3. right  |   .0421067   .0545013     0.77   0.440    -.0647591    .1489725
   4. DK and NR  |   -.012234   .0461236    -0.27   0.791    -.1026729    .0782049
                 |
          1.east |   .2531371   .0521748     4.85   0.000     .1508332    .3554409
        gentrust |   .0464572   .0072201     6.43   0.000     .0323001    .0606142
        1.female |   -.099572   .0359718    -2.77   0.006    -.1701053   -.0290388
          educyr |  -.0109418    .006976    -1.57   0.117    -.0246202    .0027366
   kr_inz_rate10 |  -.0037708    .003275    -1.15   0.250    -.0101925    .0026508
   kr_tod_rate10 |   .0155799   .0154268     1.01   0.313    -.0146688    .0458286
           _cons |  -.9491665   .1575023    -6.03   0.000    -1.257996   -.6403371
----------------------------------------------------------------------------------

.    
. estimates store November

. ereturn list

scalars:
               e(rank) =  20
               e(ll_0) =  -4092.655796226474
                 e(ll) =  -3897.185946049112
               e(r2_a) =  .1211551612880376
                e(rss) =  2516.192774420092
                e(mss) =  365.0095965420082
               e(rmse) =  .9368500326682342
                 e(r2) =  .1266865528852537
                  e(F) =  21.88822423628517
               e(df_r) =  2866
               e(df_m) =  19
                  e(N) =  2886

macros:
            e(cmdline) : "regress zopenindex c.idtecon i.incloss c.idtfamily i.childrenathome     i.riskgroup01 i.old    c.sttecon c.sttfamily    c.doubtstate c.protransfer ib2.leftrightcat    i.east     c.gentrust    i.female c.educyr    c.kr_inz_rat.."
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "ols"
             e(depvar) : "zopenindex"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
               e(wexp) : "= persweight"
              e(wtype) : "iweight"

matrices:
                  e(b) :  1 x 27
                  e(V) :  27 x 27

functions:
             e(sample)   

. 
. suest May November, vce(cluster id)

Simultaneous results for May, November

                                                Number of obs     =      5,854

                                     (Std. Err. adjusted for 3,613 clusters in id)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
May_mean         |
         idtecon |  -.0387569   .0235705    -1.64   0.100    -.0849541    .0074404
       1.incloss |   .0411791   .0618868     0.67   0.506    -.0801169     .162475
       idtfamily |  -.0151395   .0236438    -0.64   0.522    -.0614804    .0312014
1.childrenathome |  -.0126736   .0511029    -0.25   0.804    -.1128333    .0874862
   1.riskgroup01 |  -.3525135   .0430146    -8.20   0.000    -.4368205   -.2682065
           1.old |  -.0691034   .0605622    -1.14   0.254    -.1878032    .0495964
         sttecon |   .0477287   .0305146     1.56   0.118    -.0120788    .1075362
       sttfamily |   .0850086    .027691     3.07   0.002     .0307352     .139282
      doubtstate |   .1830349   .0185427     9.87   0.000     .1466918    .2193779
     protransfer |    -.01333   .0081645    -1.63   0.103    -.0293321    .0026721
                 |
    leftrightcat |
        1. left  |  -.1259192   .0542847    -2.32   0.020    -.2323152   -.0195232
       3. right  |  -.0182548   .0695029    -0.26   0.793     -.154478    .1179685
   4. DK and NR  |  -.0548026   .0531563    -1.03   0.303    -.1589871    .0493819
                 |
          1.east |   .3038457   .0435537     6.98   0.000      .218482    .3892094
        gentrust |   .0386436   .0092957     4.16   0.000     .0204242    .0568629
        1.female |  -.0797826   .0408019    -1.96   0.051    -.1597529    .0001877
          educyr |   .0014154   .0078359     0.18   0.857    -.0139428    .0167735
   kr_inz_rate10 |  -.0095933   .0364354    -0.26   0.792    -.0810054    .0618188
   kr_tod_rate10 |   .0125349   .0214151     0.59   0.558     -.029438    .0545077
           _cons |  -.9662276   .1846086    -5.23   0.000    -1.328054   -.6044014
-----------------+----------------------------------------------------------------
May_lnvar        |
           _cons |  -.0959572   .0262601    -3.65   0.000     -.147426   -.0444884
-----------------+----------------------------------------------------------------
November_mean    |
         idtecon |  -.0215578   .0224276    -0.96   0.336    -.0655151    .0223995
       1.incloss |  -.0992317   .0619104    -1.60   0.109    -.2205739    .0221104
       idtfamily |  -.0246457   .0224163    -1.10   0.272    -.0685808    .0192895
1.childrenathome |   .0544339   .0526665     1.03   0.301    -.0487906    .1576584
   1.riskgroup01 |  -.1933677   .0417735    -4.63   0.000    -.2752423    -.111493
           1.old |     .01391   .0576037     0.24   0.809    -.0989911    .1268111
         sttecon |    .072711   .0305407     2.38   0.017     .0128523    .1325696
       sttfamily |   .0723234   .0284293     2.54   0.011     .0166029    .1280438
      doubtstate |   .1989073   .0187414    10.61   0.000     .1621748    .2356398
     protransfer |  -.0253399   .0075446    -3.36   0.001    -.0401271   -.0105526
                 |
    leftrightcat |
        1. left  |  -.0547614   .0523273    -1.05   0.295     -.157321    .0477982
       3. right  |   .0421067   .0639321     0.66   0.510    -.0831979    .1674113
   4. DK and NR  |   -.012234   .0519797    -0.24   0.814    -.1141123    .0896443
                 |
          1.east |   .2531371   .0454343     5.57   0.000     .1640874    .3421867
        gentrust |   .0464572   .0082544     5.63   0.000     .0302789    .0626354
        1.female |   -.099572   .0405491    -2.46   0.014    -.1790469   -.0200971
          educyr |  -.0109418   .0078043    -1.40   0.161    -.0262381    .0043544
   kr_inz_rate10 |  -.0037708   .0035805    -1.05   0.292    -.0107886    .0032469
   kr_tod_rate10 |   .0155799   .0180076     0.87   0.387    -.0197143    .0508741
           _cons |  -.9491665   .1757189    -5.40   0.000    -1.293569   -.6047637
-----------------+----------------------------------------------------------------
November_lnvar   |
           _cons |  -.1304641   .0250731    -5.20   0.000    -.1796064   -.0813218
----------------------------------------------------------------------------------

. 
. coefplot (May, msymb(o) mcolor(blue) ciopts(lcolor(blue)) drop(_cons) ) ///
>     (November, msymb(t) mcolor(red*1.2) ciopts(lcolor(red*1.2)) drop(_cons) ) , ///
>         xline(0, lwidth(medthin) lcolor(black)) ///
>     headings(idtecon = "{bf:Individual threat:}" ///
>                  sttecon = "{bf:Sociotropic threat:}" ///
>                          doubtstate = "{bf:Political orientations:}" ///
>                          1.east = "{bf:Control variables:}" ///
>                          , labsize(2.5)) ///
>         coeflabels(idtfamily = "Family situation" ///
>                    1.childrenathome = "Children in household" ///
>                            idtecon = "Economic situation" ///
>                            1.incloss = "Income loss in pandemic" ///
>                            1.riskgroup01 = "Covid-19 at-risk group" ///
>                            badhealth = "Bad health" ///
>                            1.old = "Age >70" ///
>                            sttfamily = "Family situation" ///
>                            sttecon = "Economic situation" ///
>                            doubtstate = "Distrust in institutions" ///
>                            protransfer = "Pro redistribution" ///
>                            1.leftrightcat = "Pol. ideol.: left-wing" ///
>                            3.leftrightcat = "Pol. ideol.: right-wing" ///
>                            4.leftrightcat = "Pol. ideol.: missing" ///
>                            gentrust = "General trust" ///
>                            1.east = "East Germany" ///
>                            1.female = "Gender female" ///
>                            educyr = "Education (years)" ///
>                            kr_inz_rate10 = "Infections in last 7 days/10K pop." ///
>                            kr_tod_rate10 = "Cumulated deaths/10K pop." ///
>                            , labsize(2.5)) ///
>         xlabel(-.4(.1).4, labsize(2.2)) ylabel(, labsize(2.5)) ///
>         graphregion(fcolor(gs15)) ///
>     plotlabels("May survey" "November survey") ///
>     xtitle("Effect on opposition to containments", size(2.5)) legend( size(vsmall)) ///
>         ysize(15) xsize(10)

. 
. 
. 
. 
. 
end of do-file

. 
. 
. 
end of do-file

. log close
      name:  <unnamed>
       log:  C:\Users\Felix Wolter\Documents\Corona\Analyse\Article DiehlWolter2021\Diehl Wolter Log File.log
  log type:  text
 closed on:  25 Jun 2021, 09:06:56
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
